In vitro method for the prognosis of progression of a cancer and of the outcome in a patient and means for performing said method

ABSTRACT

The present invention relates to the prognosis of the outcome of a cancer in a patient, which prognosis is based on the quantification of one or several biological markers that are indicative of the presence of, or alternatively the level of, the adaptive immune response of said patient against said cancer.

FIELD OF THE INVENTION

The present invention relates to the field of prognosis of the outcomeof a cancer in a patient.

More precisely, this invention relates to the prognosis of the outcomeof a cancer in a patient, which prognosis is based on the quantificationof one or several biological markers that are indicative of the presenceof, or alternatively the level of, the adaptive immune response of saidpatient against said cancer.

BACKGROUND OF THE INVENTION

Because cancer is the second leading cause of death, particularly inEurope and in the United States, vast amount of efforts and financialresources are being invested in developing novel therapeuticalapproaches. However, the need for reliable diagnostic and prognostictools is a rate-limiting step in the successful application of a cancertherapy. This is best manifested by the fact that most of the currentlyknown markers of cancer are poorly reliable.

To date, malignant tumors are generally classified according to the TNMsystem. The TNM (for “Tumor-Node-Metastasis”) classification system usesthe size of the tumor, the presence or absence of tumor in regionallymph nodes, and the presence or absence of distant metastases, toassign a stage to the tumor (AJCC Cancer Staging Manual, Lippincott,5^(th) edition, pp. 171-180, 1997). The assigned stage is used as abasis for selection of appropriate therapy and for prognostic purposes.When applied for staging colorectal cancers, the TNM system allows thedistinction between (T) the degree of invasion of the intestinal wall,ranging from T0 to T4, (N) the degree of lymph node involvement, rangingfrom N0 to N3 and (M) the degree of metastasis, ranging from M0 to M1.

For colorectal cancers, a stage may be assigned to the tumor alsoaccording to the Duke's classification. Duke's classification allows thedistinction between at least four main tumor stages, respectively (A)tumor confined to the bowel wall, (B) tumor extending across the bowelwall, (C) involvement of regional nodes and (D) occurrence of distantmetastases.

However, the above clinical classifications, although they are useful,are imperfect and do not allow a reliable prognosis of the outcome ofthe cancers. This is particularly true for the cancers assigned as DukeClass B, which are of a wide range of seriousness.

Instead of conventional clinical staging, it has been provided in theart a large number of biological markers, including genes and proteins,that would be potentially useful for the diagnosis or the prognosis of awide variety of cancers. Notably, it has been disclosed various methodsfor providing patterns of gene expression that would be potentiallyuseful as cancer diagnosis or prognosis tools, including for diagnosisor prognosis of colorectal cancers.

In this context, various prior art works were aimed at showing arelationship between (i) the presence of, or the expression level of,various biological markers of the host immune response and (ii) theoccurrence of a cancer or the stage of cancer development, mainly withthe view of deciphering the mechanisms that underlie the escape from theimmune response by tumour tissues, and eventually with the view ofsuggesting suitable anti-cancer immunotherapy strategies.

Illustratively, Nistico et al. (1999, Int. J. Cancer, Vol. 84: 598-603)had suggested the existence of a spontaneous immune response against theerbB-2 oncogene product in HLA-A2-positive breast cancer patient, theefficacy of which might be dependent on tumor HLA-class-I moleculeexpression and on CD3+-T-lymphocyte localization, i.e. in intratumoral(IT) or peritumoral (PT) tissue. According to these authors, theseresults could lead to the identification of new parameters that might beuseful for defining more specific and more effective immunotherapeuticstrategies against breast cancer.

Philips et al. (2004, British Journal of Surgery, Vol. 91: 469-475) hadshown that tumour-infiltrating lymphocytes in colorectal cancer withmicrosatellite instability were activated and cytotoxic, by assayingboth (i) the CD8/CD3 mRNA ratios and (ii) the CD3, CD4, CD8, IL-2Rα andGranzyme B protein production in the tumor tissue, although there was nosignificant correlation between T cell markers mRNA copy numbers andimmunohistochemical counts. These authors suggested that, in colorectalcancer with microsatellite instability, immunogenic mutated peptidesmight be produced, that would induce an antitumor immune response, andconclude that the said cancer model might help in understanding thehost-tumour interactions, notably in view of improving immunotherapeuticstrategies.

Maki et al. (2004, J. Gastroenterolgy and Hepatology, Vol. 19:1348-1356) had shown an impairment of the cellular immune system inhepatocellular carcinoma-bearing patients, that was assessed by adecreased CD3ζ and CD28 protein expression by T cells, as well as by anincreased caspase-3 activity in CD28 down-modulated T cells, suggestingthe occurrence of a T cell apoptosis in HCC patients. According to theseauthors, a new modality of antitumor immune therapy might beestablished, that would be aimed at activating such T cells and preventthem from apoptosis. A CD3ζ decreased expression in T cells infiltratingcervical carcinoma has also been reported by Grujil et al. (1999,British Journal of Cancer, Vol. 79(⅞): 1127-1132). These authorssuggested that, in order for vaccination strategies to be successful, itmight be essential to first identify and counteract mechanisms leadingto this loss of CD3ζ.

Impairment of the host immune response, through the assessment of theexpression of CD3, CD4, CD8 and Fas Ligand proteins ontumor-infiltrating lymphocytes (TILs), was also shown in patients withoral carcinoma (Reichert et al. (2002, Clinical Cancer Research, Vol. 8:3137-3145). Similar observations were made by Prado-Garcia et al. (2005,Lung Cancer, Vol. 47: 361-371) who had studied the evasion mechanisms oflung adenocarcinoma measured the percentages of CD3+, CD4+ and CD8+cells in peripheral blood and pleural effusion, and further CD27, CD28,CD45RO, CD45RA, granzyme A, Fas and perforin protein expression in theCD8+ T cell subsets. These authors had found a blocking of the immuneresponse and suggested that further studies were needed forunderstanding the various mechanisms whereby adenocarcinoma cellsinhibit CD8+ T cells in the initiation, growth and invasion processes oflung carcinoma, with the view of developing improved treatments for lungmalignancies.

Similar observations were made by Kuss et al. (2003, British Journal ofCancer, Vol. 88: 223-230) who determined an expanded CD8+CD45RO-CD27−effector T cell subset endowed with a dysfunctional TcR signalling, insquamous cell carcinoma-bearing patients. These authors suggestedfurther studies for confirming directly the hypothesis that would linkthe observed signalling defects with apoptosis and rapid lymphocyteturnover in patients with cancer.

Also, Valmori et al. (2002, Cancer Research, Vol. 62: 1743-1750) havefound that the presence of a CD45RA+CCR7−CD8+ PBL T cell subset havingcytolytic activity in melanoma patients. These authors suggested thatimproved anti-tumor vaccination should be aimed at stimulating andmaintaining such an effector immune response early in the course of thedisease, at a time when such a response might be effective to eradicateminimal residual disease and prevent relapses.

The prior works related above disclose the use of numerous biologicalmarkers of the immune response in the course of understanding the immuneresponse mechanisms against various cancers. However, these prior worksprovide no data relating to a statistical significant relationshipbetween (i) the presence of, or the expression level of, thesebiological markers and (ii) a prognosis of the outcome of the disease.

Other studies have presented data establishing a statistical correlationbetween the expression of biological markers of the immune response fromthe host and the outcome of various cancers.

Illustratively, Ishigami et al. (2002, Cancer, Vol. 94 (5): 1437-1442)showed that reduced CD3-ζ expression negatively correlated with lymphnode involvement, depth of invasion, and clinical stage of gastriccarcinoma. Notably, these authors had shown that a reduced CD3-ζexpression correlate with a reduced 5-year survival rate of thepatients, but only for patients which were diagnosed as “Stage IV” ofgastric carcinoma.

Oshokiri et al. (2003, Journal of Surgical Ontology, Vol. 84: 224-228)showed a statistical linkage between the infiltration of a cancer cellnest by CD8+ T cells and the survival of patients affected withextrahepatic bile duct carcinoma (EBDC). These authors showed thatintratumoral CD8+ T cell immunoreactivity demonstrated a significantcorrelation with (i) fewer lymph node metastasis, (ii) reduced venousand perineural invasion, and (iii) better pTNM staging values. Thus,these authors showed that the level of CD8+ T cell infiltrationcorrelated well with the conventional pTNM clinicopathological methodand that the said biological marker was reliable for predicting thesurvival of patients with EBDC.

Also, Diederischen et al. (2003, Cancer Immunol. Immunother., Vol. 52:423-428) showed that colorectal patients with low CD4+/CD8+ ratios inTILs had a better clinical course, with significantly higher 5-yearsurvival, independent of the Dukes stage and age.

Additionally, Zhang et al. (2003, New England Journal of Medicine, Vol.348(3): 203-213) showed, by immunostaining for CD that the presence orabsence of intratumoral T cells correlates with the clinical outcome ofadvanced ovarian carcinoma after debulking and adjuvant chemotherapy.These results were obtained through immunostaining assays of tumorcryosections with monoclonal antibodies against CD3, CD4, CD8, CD83,CD45, CD45RO, CD19, CD57 and CD11c, as well as through flow cytometry ofcells from fresh tumor samples using monoclonal antibodies againstHLADR, CD3, CD4, CD8, CD16, CD19, CD45, IgG1 and IgG2a. These authorshad detected the presence or absence of CD3+ tumor-infiltrating T cellswithin tumor-cell islets and in peritumoral stroma. These authors havefound that patients whose tumors contained T cells had both a medianduration of (i) progression-free survival and (ii) overall survivalwhich was statistically higher than patients whose tumors did notcontain T cells. These authors suggested to further validate the use ofdetection of intratumoral T cells in the classification and treatment ofpatients with ovarian carcinoma.

Although the prior art works reported above disclose good correlationbetween (i) the presence of, or the level of, some biological markers ofthe immune response and (ii) the outcome of cancers, the results of mostof these prior art studies also show that the use of the said biologicalmarkers were viewed exclusively as a confirmation of a cancer stagingwith conventional clinicopathological staging methods, or as anadditional information to the said conventional cancer staging methods.For example, the biological marker used by Ishigami et al. (2002, Supra)was found to be useable exclusively with gastric carcinoma-bearingpatients who where already diagnosed as “Stage IV” of the disease.Similarly, Zhang et al. (2003, Supra) concluded that prospective studieswere needed to validate detection of intratumoral (CD3+) T cells in theclassification and treatment of patients with ovarian carcinoma.Similarly, Diederichsen et al. (2003, Supra) disclosed the CD4+/CD8+ratio as a biological marker having a survival prognostic value incolorectal cancer. However, these authors did not suggest that the saidbiological marker might be sufficient per se for cancer prognosis,without simultaneous staging data generated by conventionalclinicopathological staging methods.

Only Oshikiri et al. (2003, Supra) considered that the biological markerthat they have used, namely the infiltration of a cancer cell nest byCD8+ T cells, would consist of a reliable marker for longer survival ofpatients with EBDC, since, notably, the said marker correlated well withpTNM staging values. However, Oshikiri et al. only used the saidbiological marker as a confirmation of a prior cancer staging by aconventional clinicopathological staging method. Further, thestatistical correlation values found by Oshikiri et al. (2003) between(a) the number of intratumoral CD8+ T cells and (b) various clinicalparameters like (i) fewer lyph node metastasis (P=0.005), (ii) reducedvenous invasion (P=0.0021), (iii) reduced perineural invasion (P=0.0083)and (iv) better pTNM staging values (P=0.0356), were objectively toomuch low to suggest the one skilled in the art to make use of thisbiological marker for an accurate and reliable cancer prognosis withoutthe concomitant use of conventional clinicopathological staging data.

There is thus no disclosure in the art of reliable methods of cancerprognosis that would make use exclusively of biological markers of theadaptive immune response from the host, without a need for concomitantclinicopathological data generated by conventional cancer stagingmethods.

Further, there is, today, no reliable marker available that would allowthe prediction of the cancer outcome, in early-stage (stage I/11)colorectal cancer patients.

There is thus a need in the art for improved methods of prognosis of theoutcome of cancers, including colorectal cancers, that would stage thedisease in a more accurate and a more reliable way than the presentlyavailable methods, that is essentially, if not exclusively,clinicopathological staging methods.

Notably, the availability of improved prognosis methods would allow abetter selection of patients for appropriate therapeutical treatments,including before and after surgery. Indeed, for numerous cancersincluding colorectal cancers, the selection of an appropriatetherapeutical treatment after surgery is guided by the histopathologicaldata provided by the analysis of the resected tumor tissue.Illustratively, for colorectal cancers, adjuvant chemotherapy treatmentsare prescribed mostly when involvement of lymph nodes is diagnosed,because of the toxicity of such treatment and its lack of benefit forthe other patients.

SUMMARY OF THE INVENTION

The present invention relates to an in vitro method for the prognosis ofpatients for progression of a cancer, which method comprises thefollowing steps:

-   -   a) quantifying, in a tumor tissue sample from said patient, at        least one biological marker indicative of the status of the        adaptive immune response of said patient against cancer; and    -   b) comparing the value obtained at step a) for said at least one        biological marker with a predetermined reference value for the        same biological marker; which predetermined reference value is        correlated with a specific prognosis of progression of said        cancer.

In some embodiments of the method, step a) consists of quantifying oneor more biological markers by immunochemical techniques, preferably intwo distinct tissues, and especially both (i) in the center of the tumor(CT) and (ii) in the invasive margin (IM).

In some other embodiments of the method, step a) consists of quantifyingone or more biological markers by gene expression analysis in the wholetumor tissue sample.

This invention also relates to a kit for the prognosis of progression ofa cancer in a patient, which kit comprises means for quantifying atleast one biological marker indicative of the status of the adaptiveimmune response of said patient against cancer.

This invention also pertains to a kit for monitoring the effectivenessof treatment (adjuvant or neo-adjuvant) of a subject with an agent,which kit comprises means for quantifying at least one biological markerindicative of the status of the adaptive immune response of said patientagainst cancer.

DESCRIPTION OF THE FIGURES

FIG. 1 illustrates results of expression of inflammatory-, immunesuppressive- and immune adaptive-related genes in a series of 75colorectal cancers according to the VELIPI status and relapse. RelativemRNA expression levels were normalized to the level of 18S mRNA for eachsample. The levels are represented as fold increase (%) compared to thereference group of invasion positive (VELIPI+) patients that experienceda relapse. **: P<0.05 compared to the reference group.

FIG. 2 shows the mean±SEM of CD45RO+ cells/mm² in the different groupsof patients (N and M stages, according to the AJCC/UICC TNM staging, na:not applicable). Statistical analyses were performed using Mann-Whitneytest. * Represent significant differences (P<0.05).

FIG. 3A-B shows Kaplan-Meier overall survival (OS) and disease freesurvival (DFS) curves in CD45RO-hi (>250 CD45RO+ cells/mm², n=176, upperline), and in CD45RO-lo (<250 CD45RO+ cells/mm², n=160, bottom line).

FIG. 4 shows a comparison of immune population densities in the center(CT) and the invasive margin (IM) of the tumours from patientswith-(black histogram) or without-relapse (white histogram).

FIG. 5 The significance of all the cut-off were plotted as a function ofthe number of CD3_(CT)/mm² (black), and CD3_(IM)/mm² (green) for overallsurvival. Cut-offs for 25, 50 and 75% of the cohort are also representedfor both regions. P-values above the horizontal line (P=0.05) are allsignificant.

FIG. 6 shows the Median DFS of patients with high-(bottom histograms) orlow-densities (upper histograms) of adaptive immune cells in each tumourregion (CT or IM) is represented.

FIG. 7A-C shows Kaplan-Meier curves for the duration of DFS (7A) and OS(7B) according to the presence of High-CD3 density in the center of thetumour (CD3_(CT) ^(Hi)) and High-CD3 density in the invasive margin(CD3_(IM) ^(Hi)) (thick-grey), High-CD3_(CT) and Low-CD3_(IM)(thin-grey), Low-CD3_(CT) and High-CD3_(IM) (thin-black), andLow-CD3_(CT) and Low-CD3_(IM)(thick-black), in 415 patients withcolorectal cancer (logrank statistical test, P<10⁻⁴ for OS and DFS; **P<10⁻⁴, * P<0.05). FIG. 7C shows the combined tumour regions analysis ofadaptive immune markers. Median DFS of patients with high-(bottomhistograms) or low-densities (upper histograms) of adaptive immune cellsin both tumour regions (CT plus IM) is represented.

FIG. 8A-D

(8A): Kaplan-Meier curves for the duration of DFS according to the Dukesstages (Dukes A: red (n=75), B: green (n=137), C: blue (n=99), and D:black line (n=95)) in 415 patients with colorectal cancer.

(8B): Kaplan-Meier curves for the duration of DFS according to the Dukesstages (as in 8a) and to the presence of Low-CD3_(CT) plus Low-CD3_(IM)(thick lines, n=93) or High-CD3_(CT) plus High-CD3_(IM) (thin lines,n=109).

(8C): Kaplan-Meier curves for the duration of DFS according to the Dukesstages and to the presence of Low-CD3_(CT) plus Low-CD3_(IM) plusLow-CD45RO_(CT) plus Low-CD45RO_(IM) (thick lines, n=25) orHigh-CD3_(CT)plus High-CD3_(IM) plus High-CD45RO_(CT) plusHigh-CD45RO_(IM) (thin lines, n=87). ** P<10⁻⁴.

(8D): Kaplan-Meier curves for the duration of OS according to the Dukesstages and to the presence of Low-CD3_(CT) plus Low-CD3_(IM) plusLow-CD45RO_(CT) plus Low-CD45RO_(IM) (thick lines, n=25) orHigh-CD3_(CT) plus High-CD3_(IM) plus High-CD45RO_(CT) plusHigh-CD45RO_(IM) (thin lines, n=87).

FIG. 9: Log-rank P values for the duration of DFS according to thepresence of high-CD3+ density (CD3_(HI), group A) and low high-CD3+density (CD3^(LO), group B) in the center of the tumor (black), and inthe invasive margin of the tumor (grey). CD3+ cell densities (cell/mm²),and the number of patients in each group (A and B) are represented.P-values are significant for a large interval of cut-offs (50-1000cell/mm² in the center of the tumor, and 80-1300 cell/mm² in theinvasive margin of the tumor). The results obtained are easilyreproducible by other groups as a large range of cut-of values,(centered on the minimum P value cut-off that was determined) anddiscriminate patient outcome.

FIG. 10A-C: (FIG. 10-A) Kaplan-Meier curves for the duration ofdisease-free survival according to the UICC-TNM stages (Stages I: red(n=75), II: green (n=137), III: blue (n=99), and IV: black line (n=95))in patients with CRCs. (FIG. 10-B) Kaplan-Meier curves illustrate theduration of disease-free survival according to the UICC-TNM stages (asin panel A) and to the density of CD3+ cells in combined tumor regions(CD3_(CT) ^(Lo)CD3_(IM) ^(Lo), thick lines, n=93; CD3_(CT) ^(Hi)CD3_(IM)^(Hi), thin lines, n=109). The subgroup of patients that did not appearto have a coordinated in situ immune reaction in tumor regions (Hi/Lo orLo/Hi for CD3+ cell densities) presented similar Kaplan-Meier curves asthe entire cohort. (FIG. 10-C) Kaplan-Meier curves illustrate theduration of disease-free survival according to the UICC-TNM stages andto the density of CD3+ and CD45RO+ cells in combined tumor regions(CD3_(CTL)CD3_(IML) plus CD45RO_(CT) ^(Lo)CD45RO_(IM) ^(Lo), thicklines, n=16; CD3_(CT) ^(Hi)CD3_(IM) ^(Hi) plus CD45RO_(CT)^(Hi)CD45RO_(IM) ^(Hi), thin lines, n=88). Cut-off values were 250, 640,60, and 190 for CD3^(CT), CD3_(IM), CD45RO_(CT), and CD45RO_(IM),respectively. Log-rank statistical test, ** P<10⁻⁴.

FIG. 11A-C: Kaplan-Meier curves illustrating the duration ofdisease-free survival (A), disease-specific survival (B), and overallsurvival (C) according to the organization of CD8+ cells within thetumor regions (CT and IM) are represented. Presence of high densities ofCD8+ cells in both tumor regions (CD8-CT/IM-hi, red), of heterogeneousdensities of CD8+ cells in both tumor regions (CD8-CT/IM-het, green), oflow densities of CD8+ cells in both tumor regions (CD8-CT/IM-lo, black),in patients with stage I/II colon cancer (left) and rectum cancer(right) (log-rank statistical test, P<0.001 for all comparisons).

FIG. 12A-C Kaplan-Meier curves illustrating the duration of disease-freesurvival (A), disease-specific survival (B), and overall survival (C)according to the organization of CD45RO+ cells within the tumor regions(CT and IM) are represented. Presence of high densities of CD45RO+ cellsin both tumor regions (CD8-CT/IM-hi, red), of heterogeneous densities ofCD45RO+ cells in both tumor regions (CD45RO-CT/IM-het, green), of lowdensities of CD45RO+ cells in both tumor regions (CD45RO-CT/IM-lo,black), in patients with stage I/II colon cancer (left) and rectumcancer (right) (log-rank statistical test, P<0.001 for all comparisons).

FIG. 13: Kaplan-Meier curves illustrating the duration of disease-freesurvival according to the organization of CD45RO+ and CD8+ cells withinthe tumor regions (CT and IM) are represented. Presence of highdensities of CD45RO+ and CD8+ cells in both tumor regions(CD45RO/CD8-CT/IM-hi, red), of heterogeneous densities of CD45RO+ andCD8+ cells in both tumor regions (CD45RO/CD8-CT/IM-het, green), of lowdensities of CD45RO+ and CD8+ cells in CT region(CD45RO-CT-lo/CD8-CT-lo, blue), of low densities of CD45RO+ and CD8+cells in both tumor regions (CD45RO/CD8-CT/IM-lo, black), in 272patients with stage I/II colorectal cancer (log-rank statistical test,P<0.001 for all comparisons). The all cohort of patients with stage I/IIcolorectal cancer is represented (black dotted line).

Thus, >95% CD45RO/CD8-CT/IM-hi patients were disease-free after 18years, whereas 0% CD45RO/CD8-CT/IM-lo patients were disease-free afteronly 2 years.

FIG. 14: Kaplan-Meier curves illustrating the duration of disease-freesurvival according to the gene expression level of 6 markers (PDCD1LG1,VEGF, TNFRSF6B, IRF1, IL18RA, SELL). Four combinations are represented.(log-rank statistical test, P<0.001 for all comparisons).

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a novel method for the prognosis of theoutcome of a cancer in a patient, which novel method is based on thedetection and/or the quantification, at the tumor site, of one or morebiological markers indicative of the presence of, or alternatively ofthe level of, the adaptive immune response of said patient against saidcancer.

It has now been surprisingly shown according to the invention that aprecise determination of the in situ adaptive immune response tomalignant cancers, and especially to colorectal cancers, can be used asthe sole parameter for predicting the subsequent clinical outcome ofcancer-bearing patients, regardless of the extent of local tumorinvasion and spread to regional lymph nodes.

This statistically highly significant correlation between (i) the levelof the adaptive immune response from the patient at the tumor site and(ii) the outcome of the disease is all the more surprising that,according to the prior art knowledge, the presence of infiltratingimmune cells in mammal cancers accounted for highly variable outcomes,ranging from deleterious inflammatory processes to beneficial adaptiveimmune responses.

Further, the said highly significant correlation that is surprisinglyfound according to the invention now allows determination of theprognosis of the outcome of a cancer in a patient without furtherneeding clinicopatholigal data that are provided by the conventionalclinicopathological cancer staging methods known in the art, like Duke'sor Gruji's methods.

As it will be detailed further, when determining statistical correlationbetween (i) the presence of, or the level of, one or more biologicalmarkers of the adaptive immune response, as disclosed in thespecification and (ii) the actual outcome of cancer in patients,encompassing Disease-free survival (DFS) and Overall survival (OS), Pvalues of more than 10⁻⁸ have been obtained according to the invention,to be compared to the P values of 5×10⁻² to 1×10⁻³ that were disclosedin various prior art works like, illustratively, those of Zhang et al.(2003, Supra), Diederischen et al. (2003, Supra) or Oshikiri et al.(2003, Supra).

The applicant has found that there is a highly significant relationship(e.g. low P values) between (i) the type, density, and location ofimmune cells within tumors and (ii) the clinical outcome of thepatients, encompassing DFS and OS. This highly significant correlationhas been found when using, for assaying biological markers of theadaptive immune response, either (i) immunochemistry assays or (ii) geneexpression analysis.

By analysis of biological markers of the adaptive immune response bygene expression analysis in the whole tumor tissue sample, a high numberof significant combinations of markers were found, including numeroussignificant combinations of at least two markers, with P values of about10⁻⁴, or lower.

Importantly, it has been identified herein a dominant cluster ofco-modulated genes for T_(H1) adaptive immunity, which cluster includesTBX1 (T-box transcription factor 21), IRF1 (interferon regulatory factor1), IFNG (gamma-Interferon), CD3Z (CD3ζ), CD8, GLNY (granulysin) andGZMB (granzyme B). Further, an inverse correlation has been foundbetween expression of these genes and tumor recurrence.

Another highly significant cluster of genes according to the inventionincludes PDCD1LG1, VEGF, TNFRSF6B, IRF1, IL8RA and SELL.

By analysis of biological markers of the adaptive immune response byimmunohistochemical analysis, either (i) in the center of the tumor(CT), (ii) in the cellular environment surrounding the tumor, which mayalso be termed the “invasive margin” (IM) or (iii) in both CT and IM, anumber of significant combinations of markers were also found. Higheststatistical correlation values were found when the biological markerswere quantified both in the center of the tumor (CT) and in the invasivemargin (IM).

Firstly, it has been found according to the invention that there is ahigh correlation between a high density of T cells at the tumor site anda favorable outcome of the disease. Particularly, it has been shown thata positive outcome of the cancer is highly correlated with thequantification of a high density of CD3+ cells, CD8+ cells, CD45RO+cells or Granzyme-B+ cells at the site of the tumor, either in thecentral part of the tumor or in the invasive margin thereof.

Secondly, it has been found that the determination of the presence ofhigh densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+cells at the site of the tumor is highly correlated with reduced cancerrecurrence and/or delayed cancer recurrence and/or a lack of cancerrecurrence.

Thirdly, it has been found that the determination of the presence ofhigh densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+cells at the site of the tumor is highly correlated with reducedconcomitant distant metastasis, or a lack of concomitant distantmetastasis (M-stages).

Fourthly, it has been found that the determination of the presence ofhigh densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+cells at the site of the tumor is highly correlated with reduced earlymetastasis, or a lack of early metastasis (VE or LI or PI).

Fifthly, it has been found that the determination of the presence ofhigh densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+cells at the site of the tumor is highly correlated with a reducedinvasion of the regional lymph nodes with tumor cells (N-stages).

Sixthly, it has been found that the determination of the presence ofhigh densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+cells at the site of the tumor is highly correlated with a reducedinvasion through the intestinal wall (T-stages).

More generally, it has been found that the absence of earlydissemination of tumor manifested by tumor emboli in lymphovascular andperineural structures is markedly associated with the presence of astrong in situ immune response, said strong immune response beingillustrated, notably, by the high immune cell densities found at thetumor site, as well as by the high expression level of various genesassociated with immunity at the tumor site.

Further, it has been found according to the invention that the detectionof a strong adaptive immune response at two distinct regions of thetumor, the center of the tumor (CT) plus the invasive margin of thetumor (IM), was highly correlated with a long disease-free survival timeand overall survival time of the patients, and significantly moreinformative for prognosis of patient of progression of cancer.

Importantly, It has been found herein a high correlation between (i) thecell density of a specific type of cells form the immune system, asassayed in an immunohistochemical assay using a single biologicalmarker, and (ii) DFS or OS, with P values of at least as low as 10⁻⁷,when the said biological marker is assayed both in the center of thetumor (CT) and in the invasive margin (IM).

Generally; it has been found according to the invention that the type,the density, and the location of immune cells in cancer patients, asassayed through the presence of, or the level of, biological markers ofthe adaptive immune response, has a prognostic value that is superiorand independent of those of conventional clinicopathological cancerstaging methods, including the Duke's and the UICC-TNM classifications.

Even more specifically, the present invention now provides prognosticmethods and technical means for predicting the outcome of a cancer in apatient, particularly for cancers at an early stage of the disease, thathave proved to be far more accurate than the conventionalclinicopathological cancer staging methods, and further especially forcancers initially classified as StageI/III according to Duke'sclassification.

Thus, it has been found according to the invention that the detection ofa strong adaptive immune response at the tumor site was highlycorrelated with a long disease-free survival time (DFS) and overallsurvival time (OS) of the patients.

Thus, a first object of the present invention consists of an in vitromethod for the prognosis of progression of a cancer in a patient, whichmethod comprises the following steps:

-   -   a) quantifying, in a tumor tissue sample from said patient, at        least one biological marker indicative of the status of the        adaptive immune response of said patient against cancer; and    -   b) comparing the value obtained at step a) for said at least one        biological marker with a predetermined reference value for the        same biological marker; which predetermined reference value is        correlated with a specific prognosis of progression of said        cancer.

Unexpectedly, it has been found according to the invention that a strongcoordinated adaptive immune response correlated with an equallyfavorable cancer prognosis.

Still unexpectedly, it has been found that said correlation foundaccording to the invention was independent of the tumor invasion throughthe intestinal wall and extension to the local lymph-nodes (Duke'sclassification A, B, C).

Conversely, it has been surprisingly found that a weak in situ adaptiveimmune response correlated with a very poor prognosis, even in patientswith minimal tumor invasion (Duke's classsification A).

Thus, the criteria used according to the cancer prognosis method of theinvention, namely the status of the adaptive immune response of thecancer patient, appear not only different from those of the T, N, M andDuke's classification, but are also more precise in predicting disease(disease-free interval and survival time).

Thus, it has been found for the first time according to the inventionthat the measure of the level of the adaptive immune response of acancer-bearing patient can be used as the sole measure for predictingthe outcome of the cancer disease, without any requirement of furtherdata, and particularly without any requirement for clinicopathologicaldata provided by conventional cancer staging methods.

Indeed, although various prior art works had pointed out the possiblerelevance of marker(s) of the adaptive immune response for cancerprognosis, these prior works contained only data that might be used as aconfirmation or as an additional information to the prognosis datafurnished by the conventional cancer staging methods. Thus, no prior artworks disclosed nor suggested any reliable or reproducible in vitrocancer prognosis method that would be based exclusively on themeasurement of one or more biological markers indicative of the adaptiveimmune response of the cancer-bearing patients.

It has also been found that the detection of a strong adaptive immuneresponse at the tumor site was a reliable marker for a plurality ofcancers, like colon cancers as well as rectum cancers.

Performing the cancer prognosis method of the invention may alsoindicate, with more precision than the prior art methods, those patientsat high-risk of tumor recurrence who may benefit from adjuvant therapy,including immunotherapy.

As intended herein, the expression “prognosis of progression of acancer” encompasses the prognosis, in a patient wherein the occurrenceof a cancer has already been diagnosed, of various events, including:

-   -   (i) the chances of occurrence of metastasis;    -   (ii) the chances of occurrence of loco-regional recurrence of        cancer, including colorectal cancer; and    -   (iii) the chances of occurrence of a long disease-free (DFS)        and/or long overall survival (OS) times; i.e. a DFS time or an        OFS time of 5 years or more following testing with the in vitro        prognosis method according to the invention.

As intended herein, a “tumor tissue sample” encompasses (i) a globalprimary tumor (as a whole), (ii) a tissue sample from the center of thetumor, (iii) a tissue sample from the tissue directly surrounding thetumor which tissue may be more specifically named the “invasive margin”of the tumor, (iv) lymphoid islets in close proximity with the tumor,(v) the lymph nodes located at the closest proximity of the tumor, (vi)a tumor tissue sample collected prior surgery (for follow-up of patientsafter treatment for example), and (vii) a distant metastasis.

Preferably, when step a) consists of the expression analysis of one ormore genes, i.e. one or more pertinent biological markers, then thequantification of the expression of the said one or more genes isperformed from the whole tumor tissue sample.

Preferably, when step a) consists of the assessment of specific immunecell densities, by immunohistochemical assays for one or morecell-expressed biological makers, then the quantification of the of thesaid one or more biological markers is performed separately in at leasttwo distinct tumor tissue samples, among the tumor tissue samplesnumbered (i) to (vi) above. Most preferably, according to thisembodiment, the quantification of the said one or more biologicalmarkers is performed separately in both (i) in the center of the tumor(CT) and (ii) in the invasive margin (IM).

A tumor tissue sample, irrespective of whether it is derived from thecenter of the tumor, from the invasive margin of the tumor, or from theclosest lymph nodes, encompasses pieces or slices of tissue that havebeen removed from the tumor center of from the invasive marginsurrounding the tumor, including following a surgical tumor resection orfollowing the collection of a tissue sample for biopsy, for furtherquantification of one or several biological markers, notably throughhistology or immunohistochemistry methods, through flow cytometrymethods and through methods of gene or protein expression analysis,including genomic and proteomic analysis. It will be appreciated thattumor tissue samples may be used in the cancer prognosis method of thepresent invention. In these embodiments, the level of expression of thebiological marker can be assessed by assessing the amount (e.g. absoluteamount or concentration) of the biological marker in a tumor tissuesample, e.g., tumor tissue smear obtained from a patient. The cellsample can, of course, be subjected to a variety of well-knownpost-collection preparative and storage techniques (e.g., nucleic acidand/or protein extraction, fixation, storage, freezing, ultrafiltration,concentration, evaporation, centrifugation, etc.) prior to assessing theamount of the biological marker in the sample. Likewise, tumor tissuesmears may also be subjected to post-collection preparative and storagetechniques, e.g., fixation.

As intended herein, the “adaptive immune response” encompasses thepresence or the activity, including the activation level, of cells fromthe immune system of the host cancer patient locally at the tumor site.

As intended herein, the expression “the adaptive immune response of saidpatient against said tumor” encompasses any adaptive immune response ofsaid patient through direct (TCR-dependent) or indirect(TCR-independent), or both, action towards said cancer.

The adaptive immune response means the specific immune response of thehost cancer patient against the tumor and encompasses the presence of,the number of, or alternatively the activity of, cells involved in thespecific immune response of the host which includes: As used herein, theT lymphocytes encompass T helper lymphocytes, including Th1 and Th2 Thelper lymphocytes cell subsets.

As used herein, the T lymphocytes also encompass T cytototoxiclymphocytes.

Adaptive Immunity

In comparison to innate immunity, acquired (adaptive) immunity developswhen the body is exposed to various antigens and builds a defense thatis specific to that antigen.

The adaptive immune response is antigen-specific and may take days orlonger to develop. Cell types with critical roles in adaptive immunityare antigen-presenting cells including macrophages and dendritic cells.Antigen-dependent stimulation of T cell subtypes, B cell activation andantibody production, and the activation of macrophages and NK cells allplay important roles in adaptive immunity. The adaptive immune responsealso includes the development of immunological memory, a process thatcontinues to develop throughout life and enhances future responses to agiven antigen.

Lymphocytes, a special type of white blood cell, contain subgroups, Band T lymphocytes, that are key players in acquired immune responses. Blymphocytes (also called B cells) produce antibodies. Antibodies attachto a specific antigen and make it easier for the phagocytes to destroythe antigen. T lymphocytes (T cells) attack antigens directly, andprovide control of the immune response. B cells and T cells develop thatare specific for ONE antigen type. When you are exposed to a differentantigen, different B cells and T cells are formed.

As lymphocytes develop, they normally learn to recognize the body's owntissues (self) as distinctive from tissues and particles not normallyfound in your body (non-self). Once B cells and T cells are formed, afew of those cells will multiply and provide “memory” for the immunesystem. This allows the immune system to respond faster and moreefficiently the next time you are exposed to the same antigen, and inmany cases will prevent you from getting sick. For example, adaptiveimmunity accounts for an individual who has had chickenpox for beingso-called ‘immune’ to getting chickenpox again.

Adaptive Immune System

The adaptive immune system, also called the acquired immune system,explains the interesting fact that when most mammals survive an initialinfection by a pathogen, they are generally immune to further illnesscaused by that same pathogen. This fact is exploited by modern medicinethrough the use of vaccines. The adaptive immune system is based onimmune cells called leukocytes (or white blood cells) that are producedby stem cells in the bone marrow. The immune system can be divided intotwo parts. Many species, including mammals, have the following type:

The humoral immune system, which acts against bacteria and viruses inthe body liquids (such as blood). Its primary means of action areimmunoglobulins, also called antibodies, which are produced by B cells(B means they develop in the bone marrow).

The cellular immune system, which takes care of other cells that areinfected by viruses. This is done by T cells, also called T lymphocytes(T means they develop in the thymus). There are two major types of Tcells:

Cytotoxic T cells (TC cells) recognize infected cells by using T-cellreceptors to probe the surface of other cells. If they recognize aninfected cell, they release granzymes to signal that cell to becomeapoptotic (“commit suicide”), thus killing that cell and any viruses itis in the process of creating.

Helper T cells (TH cells) interact with macrophages (which ingestdangerous material), and also produce cytokines (interleukins) thatinduce the proliferation of B and T cells.

In addition, there are Regulatory T cells (Treg cells) which areimportant in regulating cell-mediated immunity.

Cytotoxic T cells: a cytotoxic (or TC) T cell is a T cell (a type ofwhite blood cell) which has on its surface antigen receptors that canbind to fragments of antigens displayed by the Class I MHC molecules ofvirus infected somatic cells and tumor cells. Once activated by aMHC-antigen complex, TC cells release the protein perforin, which formspores in the target cell's plasma membrane; this causes ions and waterto flow into the target cell, making it expand and eventually lyse. TCalso release granzyme, a serine protease, that can enter target cellsvia the perforin-formed pore and induce apoptosis (cell death). Most TCcells have present on the cell surface the protein CD8, which isattracted to portions of the Class I MHC molecule. This affinity keepsthe TC cell and the target cell bound closely together duringantigen-specific activation. TC cells with CD8 surface protein arecalled CD8+ T cells.

Helper (or TH) T cells: a helper (or TH) T cell is a T cell (a type ofwhite blood cell) which has on its surface antigen receptors that canbind to fragments of antigens displayed by the Class II MHC moleculesfound on professional antigen-presenting cells (APCs). Once bound to anantigen, the TH cell proliferates and differentiates into activated THcells and memory TH cells. Activated TH cells secrete cytokines,proteins or peptides that stimulate other lymphocytes; the most commonis interleukin-2 (IL-2), which is a potent T cell growth factor.Activated, proliferating TH cells can differentiate into two majorsubtypes of cells, Th1 and Th2 cells. These subtypes are defined on thebasis of specific cytokines produced. Th1 cells produce interferon-gammaand interleukin 12, while Th2 cells produce interleukin-4, interleukin-5and interleukin-13. Memory TH cells are specific to the antigen theyfirst encountered and can be called upon during the secondary immuneresponse. Most TH cells have present on the cell surface the proteinCD4, which is attracted to portions of the Class II MHC molecule. Thisaffinity keeps the TH cell and the target cell bound closely togetherduring antigen-specific activation. TH cells with CD4 surface proteinare called CD4+ T cells. The decrease in number of CD4+ T cells is theprimary mechanism by which HIV causes AIDS.

Other Definitions of Relevant Terms

As used herein the expression “tumor site” means the tumor tissue itselfas well as the tissue which is in close contact with the tumor tissue,including the invasive margin of the tumor and the regional lymph nodesthat are the most close to the tumor tissue or to the invasive margin ofthe tumor.

As intended herein, the “status” of the adaptive immune responseencompasses (i) the existence of a specific immune response againstcancer at the tumor site as well as (ii) the level of said specificimmune response.

As intended herein, a “biological marker” consists of any detectable,measurable or quantifiable parameter that is indicative of the status ofthe adaptive immune response of the cancer patient against the tumor. Amarker becomes a “biological marker” for the purpose of carrying out thecancer prognosis method of the invention when a good statisticalcorrelation is found between (i) an increase or a decrease of thequantification value for said marker and (ii) the cancer progressionactually observed within patients. For calculating correlation valuesfor each marker tested and thus determining the statistical relevance ofsaid marker as a “biological marker” according to the invention, any oneof the statistical method known by the one skilled in the art may beused. Illustratively, statistical methods using Kaplan-Meier curvesand/or univariate analysis using the log-rank-test and/or a Coxproportional-hazards model may be used, as it is shown in the examplesherein. Any marker for which a P value of less than 0.05, and evenpreferably less than 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶ or 10⁻⁷ (according tounivariate and multivariate analysis (for example, log-rank test and Coxtest, respectively) is determined consists of a “biological marker”useable in the cancer prognosis method of the invention.

Biological markers include the presence of, or the number or density of,cells from the immune system at the tumor site.

Biological markers also include the presence of, or the amount ofproteins specifically produced by cells from the immune system at thetumor site.

Biological markers also include the presence of, or the amount of, anybiological material that is indicative of the expression level of genesrelated to the raising of a specific immune response of the host, at thetumor site. Thus, biological markers include the presence of, or theamount of, messenger RNA (mRNA) transcribed from genomic DNA encodingproteins which are specifically produced by cells from the immunesystem, at the tumor site.

Biological markers thus include surface antigens that are specificallyexpressed by cells from the immune system, including by B lymphocytes, Tlymphocytes, monocytes/macrophages dendritic cells, NK cells, NKT cells,and NK-DC cells, that are recruited within the tumor tissue or at itsclose proximity, including within the invasive margin of the tumor andin the closest lymph nodes, or alternatively mRNA encoding for saidsurface antigens.

Illustratively, surface antigens of interest used as biological markersinclude CD3, CD4, CD8 and CD45RO that are expressed by T cells or T cellsubsets.

For example, if the expression of the CD3 antigen, or the expression ofthe mRNA thereof, is used as a biological marker, the quantification ofthis biological marker, at step a) of the method according to theinvention, is indicative of the level of the adaptive immune response ofthe patient involving all T lymphocytes and NKT cells.

For instance, if the expression of the CD8 antigen, or the expression ofthe mRNA thereof, is used as a biological marker, the quantification ofthis biological marker, at step a) of the method according to theinvention, is indicative of the level of the adaptive immune response ofthe patient involving cytotoxic T lymphocytes.

For example, if the expression of the CD45RO antigen, or the expressionof the mRNA thereof, is used as a biological marker, the quantificationof this biological marker, at step a) of the method according to theinvention, is indicative of the level of the adaptive immune response ofthe patient involving memory T lymphocytes or memory effector Tlymphocytes.

Yet illustratively, proteins used as biological markers also includecytolytic proteins specifically produced by cells from the immunesystem, like perforin, granulysin and also granzyme-B.

Description of the In Vitro Method for Cancer Prognosis

Step a) of the Method

At the end of step a) of the method according to the invention, aquantification value is obtained for each of the at least one biologicalmarker that is used.

As it has been previously specified, specific embodiments of step a)include:

-   -   (i) quantifying one or more biological markers by immunochemical        methods, which encompass quantification of one or more protein        markers of interest by in situ immunohistocemical methods on a        tumor tissue sample, for example using antibodies directed        specifically against each of the said one or more protein        markers. In certain embodiments, the resulting quantification        values consist of the density of cells expressing each of the        protein markers in the tumor tissue sample under analysis.    -   (ii) quantifying one or more biological markers by gene        expression analysis, which encompasses quantification of one or        more marker mRNAs of interest, for example by performing a        Real-Time PCR Taqman PCR analysis.

Thus, in certain embodiments of the method, step a) consists ofquantifying, in a tumor tissue sample, the cells expressing a specificbiological marker of the adaptive immune response. Generally acombination of at least two biological markers is assayed. In theseembodiments of step a) of the method, the value obtained at the end ofstep a) consists of the number or the density of cells of the immunesystem, or cell subsets thereof, that are contained in the said tumortissue sample and that express one specific biological marker, forexample among the combination of biological markers. In theseembodiments, what is obtained at the end of step a) consists of the celldensity values found for each biological marker included in thecombination of markers. As used herein, the density of cells of interestmay be expressed as the number of these cells of interest that arecounted per one unit of surface area of tissue sample, e.g. as thenumber of these cells of interest that are counted per cm² or mm² ofsurface area of tissue sample. As used herein, the density of cells ofinterest may also be expressed as the number of these cells of interestper one volume unit of sample, e.g. as the number of cells of interestper cm³ of sample. As used herein, the density of cells of interest mayalso consist of the percentage of a specific cell subset (e.g. CD3+ Tcells) per total cells or total cell subpopulation (set at 100%). Forexample in an embodiment of the method, cells are firstly collected bymechanical dispersion from the tumor tissue sample and cells of interestare then counted by flow cytometry, optionally after labeling, forinstance by labeled surface antigen-specific antibodies, beforedetermining cell density. The inventors believe that the highstatistical relevance that they have found between (i) thequantification values of the biological markers of interest, and (ii)the outcome of the cancer disease, when assessed the said quantificationvalues are assessed by immunohistochemical methods may be explained atleast by:

-   -   a highly precise quantification method for each marker, like the        numbering of marker-expressing cells per surface area of a tumor        tissue slice, as performed from a plurality of distinct surface        areas of the said tumor tissue slice; and    -   a combined separate quantification of the said marker in more        than one kind of tissue sample, e.g. a combined quantification        of the said biological marker both (i) in the center of the        tumor (CT) and (ii) in the invasive margin (IM), it being        understood that the statistical relevance is then calculated,        e.g. by multivariate analysis, starting from the combination of        the quantification values that are measured.

In certain other embodiments of the method, step a) consists ofquantifying, in a tumor tissue sample, the expression level of one ormore marker genes of the adaptive immune response (e.g. the amount ofthe corresponding specific mRNAs). Generally, the assessment of theexpression level for a combination of at least two marker genes isperformed. In these embodiments of step a) of the method, what isobtained at the end of step a) consists of the expression level valuesfound for each marker protein(s) specifically produced by cells from theimmune system, that is included in the combination of markers.

Said expression level may also be expressed as any arbitrary unit thatreflects the amount of mRNA encoding said protein of interest that hasbeen detected in the tissue sample, such as intensity of a radioactiveor of a fluorescence signal emitted by the cDNA material generated byPCR analysis of the mRNA content of the tissue sample, including byReal-time PCR analysis of the mRNA content of the tissue sample.

Alternatively, the said expression level may be expressed as anyarbitrary unit that reflects the amount of the protein of interest thathas been detected in the tissue sample, such as intensity of aradioactive or of a fluorescence signal emitted by a labeled antibodyspecifically bound to the protein of interest. Alternatively, the valueobtained at the end of step a) may consist of a concentration ofprotein(s) of interest that could be measured by various proteindetection methods well known in the art, such as. ELISA, SELDI-TOF, FACSor Western blotting.

In certain embodiments of step a) of the cancer prognosis methodaccording to the invention, the biological marker(s) is (are) quantifiedseparately in one, or more than one, tumor tissue sample from the cancerpatient, selected from the group consisting of (i) a global primarytumor (as a whole), (ii) a tissue sample from the center of the tumor,(iii) a tissue sample from the tissue directly surrounding the tumorwhich tissue may be more specifically named the “invasive margin” of thetumor (iv) the lymph nodes located at the closest proximity of thetumor, (v) a tumor biopsie perform prior surgery (for follow-up ofpatients after treatment for example), and (vi) a distant metastasis. Inthese embodiments, quantification value that is obtained, at the end ofstep a), for each of the tumor tissue samples (i), (ii) or (iii), iscompared, at step b) of the method, with the corresponding referencevalues previously determined for each of the tumor tissue samples (i) to(vi), respectively. Obtaining, at step a) of the method, more than onequantification value for each biological marker that is used allows amore accurate final cancer prognosis than when only one quantificationvalue per biological marker is determined.

In other embodiments of the cancer prognosis method according to theinvention, quantification values for more than one biological marker areobtained, at step a) of the method. In these embodiments, step b) iscarried out by comparing, for each biological marker used, (i) thequantification value obtained at step a) for this biological marker with(ii) the predetermined reference value for the same biological marker.

In further embodiments of the cancer prognosis method according to theinvention, step a) is performed by obtaining quantification values formore than one tumor tissue sample for a single biological marker andstep a) is performed by obtaining quantification values for more thanone biological markers, which quantification values are then compared,at step b), with the corresponding predetermined reference values.

In preferred embodiments of the in vitro prognosis method of theinvention, step a) is selected from the group consisting of:

-   -   a1) quantifying the said at least one biological marker in a        tumor tissue section by immunodetection, separately both (i) in        the center of the tumor (CT) and (ii) in the invasive margin        (IM); and    -   a2) quantifying the said at least one biological marker in the        whole tumor tissue sample by gene expression analysis.

According to a first specific embodiment of the in vitro prognosismethod of the invention, step a1) is performed by quantifying at leasttwo distinct biological markers, separately both (i) in the center ofthe tumor (CT) and (ii) in the invasive margin (IM).

When, in the in vitro method of the invention, step a) consists of stepa1), then step b) is performed by comparing (i) each quantificationvalue obtained for the same biological marker, respectively in CT and IMwith (ii) the corresponding reference values, respectively for CT andIM.

According to a second specific embodiment of the in vitro prognosismethod of the invention, step a2) is performed by quantifying at leastfive distinct biological markers in the whole tissue sample.

When, in the in vitro method of the invention, step a) consists of stepa2), then step b) is performed by comparing (i) each quantificationvalue obtained for each biological marker of the said combination of atleast five distinct biological markers.

Step b) of the Method

At step b) of the method, for each biological marker used, the valuewhich is obtained at the end of step a) is compared with a referencevalue for the same biological marker, and when required with referencevalues for the center of the tumor (CT) and the invasive margin (IM),for the said same biological marker. Said reference value for the samebiological marker is thus predetermined and is already known to beindicative of a reference value that is pertinent for discriminatingbetween a low level and a high level of the adaptive immune response ofa patient against cancer, for the said biological marker. Saidpredetermined reference value for said biological marker is correlatedwith a good cancer prognosis, or conversely is correlated with a badcancer prognosis.

First Illustrative Embodiment for Predetermining a Reference Value

Each reference value for each biological marker may be predetermined bycarrying out a method comprising the steps of:

-   -   a) providing at least one collection of tumor tissue samples        selected from the group consisting of:        -   i) a collection of tumor tissue samples from cancer patients            classified as Tis, or T1, or T2, or T3 or T4 and N0, or N1,            or N2, or N3 and M0 or M1, and with no early metastasis (VE            or LI or PI) or with early metastasis, having undergone            anti-cancer treatment, and subsequently having no cancer            relapse or no cancer recurrence after the anti-cancer            treatment;        -   ii) a collection of tumor tissue samples from cancer            patients classified as Tis, or T1, or T2, or T3 or T4 and            N0, or N1, or N2, or N3 and M0 or M1, and with no early            metastasis (VE or LI or PI) or with early metastasis, having            undergone anti-cancer treatment, and subsequently having            cancer relapses or recurrences after the anti-cancer            treatment.    -   b) quantifying, for each tumor tissue sample comprised in a        collection of tumor tissue samples provided at step a), the said        biological marker, whereby a collection of quantification values        for the said biological marker and for the said collection of        tumor tissue samples is obtained;    -   c) calculating, from the said collection of quantification        values obtained at the end of step b), the mean quantification        value for the said biological marker, whereby a predetermined        reference value for said biological marker that is correlated        with a specific cancer prognosis is obtained.

The “anti-cancer treatment” that is referred to in the definition ofstep a) above relate to any type of cancer therapy undergone by thecancer patients previously to collecting the tumor tissue samples,including radiotherapy, chemotherapy and surgery, e.g. surgicalresection of the tumor.

According to the method for obtaining predetermined reference valuesabove, more than one predetermined reference value may be obtained for asingle biological marker. For example, for a single biological marker,the method above allows the determination of at least four predeterminedreference values for the same biological marker, respectively onepredetermined reference value calculated from the mean quantificationvalue obtained when starting, at step a), with each of the collections(i) and (ii) of tumor tissue samples that are described above.

Second Illustrative Embodiment for Predetermining a Reference Value

Reference values used for comparison at step b) of the method may alsoconsist of “cut-of” values that may be determined as describedhereunder.

Each reference (“cut-off”) value for each biological marker may bepredetermined by carrying out a method comprising the steps of:

-   -   a) selecting a biological marker for which a reference value is        to be determined;    -   b) providing a collection of tumor tissue samples from cancer        patients;    -   c) providing, for each tumor sample provided at step b),        information relating to the actual clinical outcome for the        corresponding cancer patient;    -   d) providing a serial of arbitrary quantification values for the        said biological marker selected at step a);    -   e) quantifying the said biological marker in each tumor tissue        sample contained in the collection provided at step b);    -   f) classifying the said tumor samples in two groups for one        specific arbitrary quantification value provided at step c),        respectively:        -   (i) a first group comprising tumor samples that exhibit a            quantification value for the said marker that is lower than            the said arbitrary quantification value contained in the            said serial of quantification values;        -   (ii) a second group comprising tumor samples that exhibit a            quantification value for the said marker that is higher than            the said arbitrary quantification value contained in the            said serial of quantification values;    -   whereby two groups of tumor samples are obtained for the said        specific quantification value, wherein the tumors samples of        each group are separately enumerated;    -   g) calculating the statistical significance between (i) the        quantification value for the said biological marker obtained at        step e) and (ii) the actual clinical outcome of the patients        from which tumor samples contained in the first and second        groups defined at step f) derive;    -   h) reiterating steps f) and g) until every arbitrary        quantification value provided at step d) is tested;    -   i) setting the said reference value (“cut-off” value) as        consisting of the arbitrary quantification value for which the        highest statistical significance (most significant) has been        calculated at step g).

The method above consists of setting a “cut-off” value at the median ofthe data sets and is fully disclosed in the examples herein.

As it is disclosed above, the said method allows the setting of a single“cut-off” value permitting discrimination between bad and good outcomeprognosis. Practically, as it is disclosed in the examples herein, highstatistical significance values (e.g. low P values) are generallyobtained for a range of successive arbitrary quantification values, andnot only for a single arbitrary quantification value. Thus, in onealternative embodiment of the method of determining “cut-off” valuesabove, a minimal statistical significance value (minimal threshold ofsignificance, e.g. maximal threshold P value) is arbitrarily set and therange of arbitrary quantification values for which the statisticalsignificance value calculated at step g) is higher (more significant,e;g. lower P value) are retained, whereby a range of quantificationvalues is provided. The said range of quantification values consist of a“cut-off” value according to the invention. According to this specificembodiment of a “cut-off” value, bad or good clinical outcome prognosiscan be determined by comparing, at step b) of the prognosis method ofthe invention, the value obtained at step a) with the range of valuesdelimiting the said “cut-off” value, for one specific biological marker.In certain embodiments, a cut-off value consisting of a range ofquantification values for the considered biological marker, consists ofa range of values centered on the quantification value for which thehighest statistical significance value is found (e;g. generally theminimum P value which is found).

In certain preferred embodiments of the method for predetermining acut-off value that is described above, the said biological markerconsists of the density of cells expressing a specific protein marker inthe tumor sample. Additionally, for a single protein marker, cut-offvalues for at least two distinct biological markers may be determined,respectively (i) a first cut-off value determined for a first biologicalmarker consisting of the density of cells expressing the said proteinmarker at the center of tumor (CT) and (ii) a second cut-off valuedetermined for a second biological marker consisting of the density ofcells expressing the said protein marker at the invasive margin (IM).

In certain preferred embodiments of step c) of the method fordetermining cut-off values above, the said information relating to theactual clinical outcome of the patients are selected from the groupconsisting of (i) the duration of the disease-free survival (DFS) and(ii) the overall survival (OS).

Indeed, for performing the cancer prognosis method according to theinvention, the availability of a predetermined reference value for morethan one biological marker is preferred. Thus, generally, at least onepredetermined reference value is determined for a plurality ofbiological markers indicative of the status of the adaptive immuneresponse against cancer that are encompassed herein, by simplyreiterating any one of the methods for obtaining predetermined referencevalues that are described above, for a plurality of biological markers.

For instance, in certain embodiments wherein the biological markerconsists of a surface antigen expressed by cells from the immune system,like the CD3 antigen, and wherein at step a) of the cancer prognosismethod a flow cytometry analysis of the CD3+ cell density at the tumorsite is carried out, the predetermined reference value may consist ofthe cell density value, including percentage of specific cells (e.g.CD3+) per total cells or total cell subpopulation (set at 100%), thatcorrelates with bad cancer prognosis, e.g. relapses or recurrences,short survival time, etc., or in contrast may consist of the celldensity value that correlates with good cancer prognosis, e;g. no earlymetastasis, no metastasis at all or long disease-free survival time.

In certain embodiments, the reference predetermined value consists of a“cut-off” value, as already disclosed above, which “cut-off” valueconsists of a median quantification value for the biological marker ofinterest that discriminates between bad cancer prognosis and good cancerprognosis. Illustratively, for human colorectal cancer, it has beenfound that, when using immunohistochemistry analysis of CD3+ cells atthe tumor site as the biological marker, the predetermined cut-offreference value may be of about 300 CD3+ cells/mm² for a tumor tissuesample collected from the center of the tumor, and that thepredetermined cut-off reference value may be of about 600 CD3+ cells/mm²for a tumor tissue sample collected from the invasion margin. In otherembodiments wherein the cut-off value consists of a range of valuesdelimiting a low and a high CD3+ quantification values, the said cut-offvalue optimally ranges from 50 CD3+ cells/mm² to 1000 CD3+ cells/mm² fora quantification in the center of the tumor (CT) and from 80 CD3+cells/mm² to 1300 CD3+ cells/mm² for a quantification in the invasivemargin (IM).

The optimal cut-off values based on log-rank tests, for CD3, CD8,CD45RO, GZMB cell densities were 370, 80, 80, 30 cells/mm² in the centerof the tumour, respectively, and 640, 300, 190, 60 cells/mm² in theinvasive margin, respectively, as shown in the examples herein.

According to the embodiments above, a bad cancer prognosis is obtainedif the quantification value generated for the CD3+ biological marker isless than the predetermined cut-off reference value, when the comparisonis carried out at step b) of the method. Conversely, a good cancerprognosis is obtained if the quantification value generated for the CD3+biological marker is more than the predetermined cut-off referencevalue, when the comparison is carried out at step b) of the method

Third Illustrative Embodiment for Predetermining a Reference Value

Also illustratively, in embodiments wherein the biological markerconsists of the expression level of a gene related to the immuneresponse of the human body, the predetermined reference value mayconsist of the gene expression value that correlates with bad cancerprognosis, e.g. relapses or recurrences, short survival time, etc., orin contrast may consist of the gene expression value that correlateswith good cancer prognosis, e.g. no metastasis at all or longdisease-free survival time. The gene expression value may be expressedas any arbitrary unit. For instance, the gene expression value may beexpressed as the difference (deltaCT) between (i) the amount of thebiological marker-specific mRNA and (ii) the amount of an unrelatedmRNA, found in the tumor tissue sample, such as for example theribosomal 18S mRNA. Illustratively, for human colorectal cancer, thedifference between (i) the amount of the biological marker-specific mRNAand (ii) the amount of an unrelated mRNA may be arbitrarily assigned toconsist of the deltaCT and of the mean of all values from the referencegroup (e.g. for patients undergoing early steps of metastasis processes(VELIPI) and relapses, set to “100%”). In these embodiments, thequantification value generated for a particular gene-specific mRNA, atstep a) of the method, is more than 100%, then a better cancer prognosisthan with the predetermined reference value is obtained. For instance,this is shown in the examples herein, when using notably CD8α-specificmRNA, GZM-B-specific mRNA and GLNY-specific mRNA.

Comparison(s) Performed at Step b)

As already specified, and as it is shown in the examples herein, step b)of the in vitro prognosis method of the invention consists of comparing,for each biological marker tested, respectively:

-   -   (i) the quantification value found at step a) for the said        biological marker; and    -   (ii) the corresponding reference value that is already        predetermined for the said biological marker.

When two or more biological markers are quantified at step a), then stepb) consists of two or more comparison steps of the kind defined above.

Also, when one specific biological marker is quantified at step a) invarious tumor locations, and especially separately both in the center ofthe tumor (CT) and in the invasive margin (IM), then step b) comprisesfor the said specific biological marker the same number of comparisonsteps than the number of tumor locations wherein the said specificbiological marker is quantified. Especially for situations wherein aspecific biological marker is quantified separately both in CT and IM atstep a), then step b) comprises, for the said specific biologicalmarker, two comparison steps, respectively:

-   -   (i) a first comparison step between the quantification value        obtained at step a) for the said biological marker in CT, with        the predetermined reference value in CT for the said biological        marker; and    -   (ii) a second comparison step between the quantification value        obtained at step a) for the said biological marker in IM, with        the predetermines reference value in IM for the said biological        marker.

Thus, step b) comprises the same number of single comparison steps thanthe number of quantification values that are obtained at step a).

The said comparison step b), irrespective of whether step a) consists ofstep a1) (immunological methods) or step a2) (gene expression analysis)as defined above, preferably also comprises the calculation of astatistical relevance of the plurality of marker quantification valuesmeasured at step a), after their comparison with the correspondingreference values, e.g. using a P Log Rank test, as disclosed in theexamples herein.

More simply, the said comparison step b) may include a classification ofthe quantification values measured at step a), for each biologicalmarker, and optionally also for each kind of tumor tissue tested, in twogroups, respectively: (i) a first group termed “Hi” when thequantification value for the said biological marker, optionally in thesaid kind of tumor tissue, is higher than the predeterminedcorresponding reference value and (ii) a second group termed “Lo” whenthe quantification value for the said biological marker, optionally inthe said kind of tumor tissue, is lower than the predeterminedcorresponding reference value. It flows that if the result of thecomparison step b) consists of exclusively “Hi” values for each markertested, then a favourable outcome prognosis for the said cancer isdetermined. Conversely, if the result of the comparison step b) consistsof exclusively “Lo” values for each marker tested, then a poor outcomeprognosis for the said cancer is determined. Intermediate conclusionsare determined for “heterogeneous” patients, wherein, in the comparisonstep b), “Hi” quantification values are found for one or more of thebiological markers tested and “Lo” quantification values are found forthe remaining markers of the combination of biological markers tested,as it is disclosed in the examples herein.

The inventors believe that a further reason for explaining the highstatistical relevance of the in vitro prognosis method according to theinvention, for predicting cancer outcome, consists of the size of thepatients cohorts that have been tested, which provide highly accuratereference values, e.g. highly accurate “cut-off” values, allowing areproducible and accurate discrimination between patients with goodprognosis and patients with bad prognosis.

Thus, in a most preferred embodiment of the in vitro method according tothe invention, the predetermined reference value for each specificbiological marker, and optionally the tumor tissue type, that is used atthe comparison step b) is calculated on the basis of quantificationvalues for the said marker, and optionally the said marker in the saidtumor tissue type, that are previously measured in tumor tissue samplesoriginating from a large population of cancer-bearing individuals.

The accuracy of a specific predetermined reference value increases withthe number of tissue samples that are used for obtaining quantificationvalues for a specific biological marker and thus for calculating a meanvalue (the predetermined reference value) which is associated with aspecific cancer outcome. Thus, another explanation for the high accuracyof the in vitro prognosis method according to the invention also residesin the high relevancy of the predetermined reference values, to whichare compared the quantification values, at step b) of the method. Mostpreferably in view of obtaining highly relevant predetermined referencevalues for each biological marker of interest, the said predeterminedreference values consist of the mean value of a plurality ofquantification values of the said marker measured on tissue samplesoriginating from the same plurality number of cancer-bearing patientswhich underwent a specific clinical outcome.

Most preferably, for assessing accurate predetermined reference values,the said reference values are predetermined from at least 50quantification values, for a specific biological marker, thus usingtissue samples originating from at least 50 cancer-bearing patients thathave underwent a specific bad or good clinical outcome, e.g. DFS or OFSof more than 5 years following diagnosis. In preferred embodiments, apredetermined reference value is obtained from at least, 60, 70, 80, 90,100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230,240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370,380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500 or morequantification values for a specific biological marker.

Illustratively, predetermined reference values disclosed in the examplesherein have been obtained from tissue samples originating from a cohortof about 500 cancer-bearing patients

The possibility according to the invention to test large cohorts ofpatients, i.e. a large number of tissue samples, was provided notably bythe use of the tissue microarray technique that is detailed further inthe present specification.

Specific embodiments of the methods used for performing step b) arefully detailed in the examples herein.

Optional Step c) of the Method

The in vitro cancer prognosis method of the invention may furthercomprise a step c) wherein the prognosis result per se is provided.

Thus, the cancer prognosis method of the invention may comprise afurther step c) wherein, depending of the biological marker(s) that areused, either:

-   -   (i) a good prognosis of patient for cancer progression is        determined, with no cancer relapse or with no cancer recurrence,        when the quantification value(s) obtained at step a) for a        specific biological marker, or a specific combination of        biological markers, is higher than, or lower than, respectively,        the corresponding predetermined reference value(s); or    -   (ii) a bad prognosis of patient for cancer progression is        determined, with cancer relapse or with cancer recurrence, when        the quantification value(s) obtained at step a) for a specific        biological marker, or a specific combination of biological        markers, is higher than, or lower than, the said corresponding        predetermined reference value(s);

Alternatively, the cancer prognosis method of the invention may comprisea further step c) wherein, depending of the biological marker(s) used,either:

-   -   (i) a good prognosis of patient for cancer progression is        determined, with high disease-free survival (DFS) value or high        overall survival (OS) value, when the quantification value(s)        obtained at step a) for a specific biological marker, or a        specific combination of biological markers, is higher than, or        lower than, the corresponding predetermined reference value(s);        or    -   (ii) a bad prognosis of patient for cancer progression is        determined, with low disease-free survival (DFS) value or low        overall survival (OS) value, when the quantification value(s)        obtained at step a) for a specific biological marker, or a        specific combination of biological markers, is lower than, or        higher than, respectively, the said corresponding predetermined        reference value(s);

Usually, for most of the biological markers used herein, thequantification value increases with an increase of the adaptive immuneresponse against cancer. For instance, when the biological marker thatis quantified at step a) consists of a protein or a gene specificallyexpressed by cells of the immune system, the quantification value ofsaid marker increases with the level of the adaptive immune responseagainst cancer of the patient tested. Thus, when performing step b) ofthe cancer prognosis method of the invention, a good prognosis isdetermined when the quantification value for a specific biologicalmarker that is obtained at step a) is higher than the correspondingpredetermined reference value, notably in embodiments wherein thepredetermined reference value consists of a cut-off value. Conversely, abad prognosis is determined when the quantification value obtained atstep a) for a specific biological marker is lower than the correspondingpredetermined reference value, notably in embodiments wherein thepredetermined reference value consists of a cut-off value.

Specific embodiments of the methods used for performing step c) arefully detailed in the examples herein.

Combinations of Biological Markers

When performing the cancer prognosis method of the invention with morethan one biological marker, the number of distinct biological markersthat are quantified at step a) are usually of less than 100 distinctmarkers, and in most embodiments of less than 50 distinct markers.

Advantageously, when high throughput screening of samples is sought, thecancer prognosis method of the invention is performed by using up to 20distinct biological markers.

The higher number of distinct biological markers are quantified at stepa) of the method, the more accurate the final cancer prognosis will be.

The number of distinct biological markers that is necessary forobtaining an accurate and reliable cancer prognosis, using the in vitroprognosis method of the invention, may vary notably according to thetype of technique for quantification which is performed at step a).

Illustratively, high statistical significance was found with acombination of a small number of biological markers, when step a) isperformed by in situ immunohistochemical detection of protein markers ofinterest, provided that separate quantification of the said markers areperformed both in the center of the tumor (CT) and in the invasivemargin (IM). Illustratively, high statistical significance was obtainedwith a combination of two to ten biological markers, as disclosed in theexamples herein. Without wishing to be bound by any particular theory,the inventors believe that highly statistical relevance (P value lowerthan 10⁻³) for cancer prognosis is reached when step a) is performed byusing an immunohistochemical method for biological markerquantification, and by using a combination of three distinct biologicalmarkers, or more.

Further illustratively, high statistical significance was also foundwith a small number of biological markers, when step a) is performed bygene expression analysis of gene markers of interest, although the geneexpression analysis technique is performed on the whole tumor sample.Illustrative embodiments of various highly significant combination oftwo gene markers are provided notably in Table 4. Without wishing to bebound by any particular theory, the inventors believe that highlystatistical relevance (P value lower than 10⁻³) is reached when step a)is performed by using a gene expression analysis for biological markerquantification, and by using a combination of ten distinct biologicalmarkers, and more preferably a combination of fifteen distinctbiological markers, most preferably twenty distinct biological markers,or more.

As it is shown in the examples herein, a reliable cancer prognosis maybe obtained when quantifying a single biological marker at step a) ofthe method, as it is illustrated, for example, with quantification ofCD3+, CD8+, CD45RO, GZM-B, GLNY, TBX21, IRF1, IFNG, CXCL9 and CXCL10biological markers.

Thus, in preferred embodiments of the cancer prognosis method accordingto the invention, the tumor tissue sample that is referred to in step a)is selected from the group consisting of (i) a global primary tumor (asa whole), (ii) a tissue sample from the center of the tumor, (iii) atissue sample from the tissue directly surrounding the tumor whichtissue may be more specifically named the “invasive margin” of thetumor, (iv) the lymph nodes located at the closest proximity of thetumor, (v) a tumor biopsie perform prior surgery (for follow-up ofpatients after treatment for example), and (vi) a distant metastasis.

Most preferably, when the in vitro prognosis method of the invention isperformed with biological markers consisting of the densities of cellsexpressing specific proteins, then step a) is performed throughimmunohistochemical techniques and cell densities are measured (i) inthe center of the tumor (CT), (ii) in the invasive margin (IM) or (iii)separately both in the CT and in the IM.

Most preferably, when the in vitro prognosis method of the invention isperformed with biological markers consisting of the expression level ofgenes of interest, then step a) is performed through gene expressionanalysis methods, like real-time Taqman PCR analysis, starting from thewhole tumor tissue that was initially collected from the cancer patient,e.g. tumor tissue originating from a tumor resection during a surgicaloperation.

Preferably, the at least one biological marker indicative of the statusof the adaptive immune response of said patient against cancer, that isquantified at step a), consists of at least one biological markerexpressed by a cell from the immune system selected from the groupconsisting of B lymphocytes, T lymphocytes, monocytes/macrophages,dendritic cells, NK cells, NKT cells, and NK-DC cells.

Preferably, said at least one biological marker, that is quantified atstep a), is selected from the group consisting of:

-   -   (i) the number or the density of cells from the immune system        contained in the tumor tissue sample and that express the said        biological marker, generally a protein marker; and    -   (ii) the expression level of a nucleic acid of interest in the        tumor tissue sample, generally the amount of mRNA encoded by a        specific gene marker.

In certain embodiments of the method, said at least one biologicalmarker consists of the density of T lymphocytes present at the tumorsite.

In certain other embodiments, said at least one biological markerconsists of the quantification value of a protein expressed by cellsfrom the immune system present at the tumor site.

In further embodiments of the method, said at least one biologicalmarker consists of the quantification value of the expression of genespecifically expressed by cells from the immune system present at thetumor site.

A list of the preferred biological markers that may be used for carryingout the cancer prognosis method of the invention are listed in Tables 2,4, 8, 9 and 10. Tables 2, _9 and 10 contain, for each biological markerthat is listed, the accession number to its nucleic acid and amino acidsequences, as available in the GenBank International database.

Although the cancer prognosis method according to the invention has beentested for colorectal cancer, said method may be applied for a widevariety of cancers. Without wishing to be bound by any particulartheory, the inventors believe that the cancer prognosis method of theinvention may be successfully carried out for prognosing the progressionof any cancer that develops from a central tumor to which cells from theimmune system have access.

Thus, the cancer prognosis method according to the invention ispotentially useful for determining the prognosis of patients forprogression of a cancer selected from the group consisting of adrenalcortical cancer, anal cancer, bile duct cancer (e.g. periphilar cancer,distal bile duct cancer, intrahepatic bile duct cancer), bladder cancer,bone cancer (e.g. osteoblastoma, osteochrondroma, hemangioma,chondromyxoid fibroma, osteosarcoma, chondrosarcoma, fibrosarcoma,malignant fibrous histiocytoma, giant cell tumor of the bone, chordoma,lymphoma, multiple myeloma), brain and central nervous system cancer(e.g. meningioma, astocytoma, oligodendrogliomas, ependymoma, gliomas,medulloblastoma, ganglioglioma, Schwannoma, germinoma,craniopharyngioma), breast cancer (e.g. ductal carcinoma in situ,infiltrating ductal carcinoma, infiltrating lobular carcinoma, lobularcarcinoma in situ, gynecomastia), Castleman disease (e.g. giant lymphnode hyperplasia, angiofollicular lymph node hyperplasia), cervicalcancer, colorectal cancer, endometrial cancer (e.g. endometrialadenocarcinoma, adenocanthoma, papillary serous adnocarcinoma, clearcell), esophagus cancer, gallbladder cancer (mucinous adenocarcinoma,small cell carcinoma), gastrointestinal carcinoid tumors (e.g.choriocarcinoma, chorioadenoma destruens), Hodgkin's disease,non-Hodgkin's lymphoma, Kaposi's sarcoma, kidney cancer (e.g. renal cellcancer), laryngeal and hypopharyngeal cancer, liver cancer (e.g.hemangioma, hepatic adenoma, focal nodular hyperplasia, hepatocellularcarcinoma), lung cancer (e.g. small cell lung cancer, non-small celllung cancer), mesothelioma, plasmacytoma, nasal cavity and paranasalsinus cancer (e.g. esthesioneuroblastoma, midline granuloma),nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngealcancer, ovarian cancer, pancreatic cancer, penile cancer, pituitarycancer, prostate cancer, retinoblastoma, rhabdomyosarcoma (e.g.embryonal rhabdomyosarcoma, alveolar rhabdomyosarcoma, pleomorphicrhabdomyosarcoma), salivary gland cancer, skin cancer (e.g. melanoma,nonmelanoma skin cancer), stomach cancer, testicular cancer (e.g.seminoma, nonseminoma germ cell cancer), thymus cancer, thyroid cancer(e.g. follicular carcinoma, anaplastic carcinoma, poorly differentiatedcarcinoma, medullary thyroid carcinoma, thyroid lymphoma), vaginalcancer, vulvar cancer, and uterine cancer (e.g. uterine leiomyosarcoma).

In yet further embodiments of the method, said at least one biologicalmarker is selected from the group consisting of the following biologicalmarkers:

(i) Various Biological Markers

ICAM-2/CD102, 4-1BB/TNFRSF9, IFN-gamma R1, IFN-gamma R2, B7-1/CD80, IL-1RI, IL-2 R alpha, BLAME/SLAMF8, IL-2 R beta, CCR3, CCR4, CCR5, CCR6,CCR7, CCR8, IL-7 R alpha, CCR9, CXCR1/IL-8 RA, CD2, CD3epsilon, CD3zeta,CD3gamma, CD4, CD4+/45RA−, IL-12 R beta 1, CD4+/45RO−, IL-12 R beta 2,CD4+/CD62L−/CD44, CD4+/CD62L+/CD441L−17, CD5, Integrin alpha 4/CD49d,CD6, Integrin alpha E/CD103, CD8, Integrin alpha M/CD11b, CD8+/45RA−,Integrin alpha X/CD11c, CD8+/45RO−, Integrin beta 2/CD18, CD27/TNFRSF7,LAG-3, CD28, LAIR1, CD30/TNFRSF8, LAIR2, CD31/PECAM-1, CD40Ligand/TNFSF5, NCAM-L1, CD43, NTB-A/SLAMF6, CD45, CD83, CD84/SLAMF5,RANK/TNFRSF11A, L-Selectin, CD229/SLAMF3, SIRP beta 1, CD69, SLAM,Common gamma Chain/IL-2 R gamma, CRACC/SLAMF7, CX3CR1, CXCR3, CXCR4,CXCR6, TNF RI/TNFRSF1A, TNF RII/TNFRSF1B, Fas/TNFRSF6, FasLigand/TNFSF6, TSLP, TSLP R, ICAM-1/CD54, IL-2, IFN-gamma, IL-4, IL-5,IL-10, IL-13,(ii) Biological Markers of Th1/Th2 Cells:II-2R Common beta Chain, Common gamma Chain/IL-2 R gamma, IFN-gamma,IFN-gamma R1, IL-12, IFN-gamma R2, IL-12 R beta 1, IL-2, IL-12 R beta 2,IL-2 R alpha, IL-2 R beta, IL-24, TNF RI/TNFRSF1A, TNF RII/TNFRSF1B,IL-4 R, TNF-beta/TNFSF1B,(iii) Biological Markers of the Interferon Family:IFN alpha, IFN beta, IFN-alpha/beta R1, IFN-alpha/beta R2, IFN-gamma R1,IFN-gamma R2, IFN-alpha A, IFN-alpha/beta R2, IFN-alpha B2, IFN-beta,IFN-alpha C, IFN-gamma, IFN-alpha D, IFN-alpha G, IFN-omega, IFN-alphaH2,(iv) Biological Markers of the Common Gamma Chain Receptor Family:Common gamma Chain/IL-2 R gamma, IL-7 R alpha, IL-2, IL-9, IL-2 R alpha,IL-9 R, IL-2 R beta, IL-15, IL-15 R alpha, IL-21, IL-7, IL-21 R, IL-31,(v) Biological Markers of the CX3C Chemokines & Receptors:CX3C Chemokine Ligands, CX3CL1/Fractalkine,CX3C Chemokine receptors, CX3CR1,(vi) Biological markers of CXC Chemokines & Receptors,CXC Chemokine Ligands, CXCL13/BLC/BCA-1, CXCL11/I-TAC, CXCL14/BRAK,CXCL8/IL-8, CINC-1, CXCL10/IP-10/CRG-2, CINC-2, CINC-3, CXCL16,CXCL15/Lungkine, CXCL5/ENA, CXCL9/MIG, CXCL6/GCP-2, CXCL7/NAP-2, GRO,CXCL4/PF4, CXCL1/GRO alpha, CXCL12/SDF-1, CXCL2/GRO beta, ThymusChemokine-1, CXCL3/GRO gamma,CXC Chemokine Receptors, CXCR6, CXCR3, CXCR1/IL-8 RA, CXCR4, CXCR2/IL-8RB, CXCR5,(vii) Biological markers of CC Chemokines & Receptors,CC Chemokine Ligands, CCL21/6Ckine, CCL12/MCP-5, CCL6/C10, CCL22/MDC,CCL28, CCL3L1/MIP-1 alpha Isoform LD78 beta, CCL27/CTACK, CCL3/MIP-1alpha, CCL24/Eotaxin-2, CCL4/MIP-1 beta, CCL26/Eotaxin-3, CCL15/MIP-1delta, CCL11/Eotaxin, CCL9/10/MIP-1 gamma, CCL14a/HCC-1, MIP-2,CCL14b/HCC-3, CCL19/MIP-3 beta, CCL16/HCC-4, CCL20/MIP-3 alpha,CCL1/I-309/TCA-3, CCL23/MPIF-1, MCK-2, CCL18/PARC, CCL2/MCP-1,CCL5/RANTES, CCL8/MCP-2, CCL17/TARC, CCL7/MCP-3/MARC, CCL25/TECK,CCL13/MCP-4CC Chemokine Receptors, CCR1, CCR7, CCR2, CCR8, CCR3, CCR9,CCR4, D6, CCR5, HCR/CRAM-A/B, CCR6(viii) Biological Markers of CC Chemokine InhibitorsCCI, CC Viral Chemokine Homologs, MCV-type II, MIP-II, MIP-I, MIP-III(ix) Biological Markers of C Chemokines & ReceptorsThe C (gamma) subfamily lacks the first and third cysteine residues.Lymphotactin (also known as SCM-1 alpha) and SCM-1 beta are currentlythe only two family members. Both have chemotactic activity forlymphocytes and NK cells.C Chemokine Ligands, XCL1/LymphotactinC Chemokine Receptors, XCR1(x) Biological Markers of Other InterleukinsIL-12, IL-12 R beta 1, IL-12 R beta 2, IL-27, IL-15, IL-31

In the present specification, the name of each of the various biologicalmarkers of interest refers to the internationally recognised name of thecorresponding gene, as found in internationally recognised genesequences and protein sequences databases, including in the databasefrom the HUGO Gene Nomenclature Committee, that is available notably atthe following Internet address:

-   -   http://www.gene.ucl.ac.uk/nomenclature/index.html

In the present specification, the name of each of the various biologicalmarkers of interest may also refer to the internationally recognisedname of the corresponding gene, as found in the internationallyrecognised gene sequences and protein sequences database Genbank.

Through these internationally recognised sequence databases, the nucleicacid and the amino acid sequences corresponding to each of thebiological marker of interest described herein may be retrieved by theone skilled in the art.

In yet further embodiments of the method, as already mentioned above,quantification values for a combination of biological markers areobtained at step a) of the cancer prognosis method of the invention.

Thus, the cancer prognosis method of the invention may be performed witha combination of biological markers. The number of biological markersused is only limited by the number of distinct biological markers ofinterest that are practically available at the time of carrying out themethod. However, a too much high number of biological markers willsignificantly increase the duration of the method without simultaneouslysignificantly improving the final prognosis determination.

Usually, in the embodiments wherein the cancer prognosis method of theinvention is performed with a combination of biological markers, notmore than 50 distinct biological markers are quantified at step a). Inmost embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49 and 50 distinct biological markers are quantified. However, asalready mentioned previously in the present specification, the number ofcombined markers that are required for reaching a high statisticalrelevance (e.g. P lower than 10⁻³), will be depending from the kind oftechnique for quantifying the said combination of biological markers, atstep a) of the in vitro prognosis method.

In certain embodiments of the in vitro prognosis method of theinvention, wherein step a) is performed by quantifying biologicalmarkers with immunohistochemical techniques, then the use of acombination of a low number of markers may be sufficiently informative,specifically if the biological markers are separately quantified both inthe center of the tumor (CT) and in the invasive margin (IM).

In certain other embodiments of the in vitro prognosis method of theinvention, wherein step a) is performed by quantifying biologicalmarkers with gene expression analysis techniques, then the use of acombination of a higher number of markers is generally required, forexample at least about 10 distinct biological markers.

In still further embodiments of the method, the said at least onebiological marker is selected from the group consisting of CD3, CD8,GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6,IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG,Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5 and CD2. These biologicalmarkers are preferably quantified, at step a) of the in vitro prognosismethod of the invention, by immunochemical methods, including in situimmunohistochemical methods. The quantification values may be expressedas the mean density of cells expressing a marker protein of interestcontained per surface area of a tissue section from the tumor tissuesample.

Illustratively, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 distinctbiological markers are quantified at step a), which biological markersare selected from the group consisting of CD3, CD8, GZMB, CD45RO, GLNY,TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta,Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ,LAT, ZAP70, CD5 and CD2. Quantification of this group of biologicalmarkers is preferably performed, at step a) of the in vitro prognosismethod of the invention, with immunohistochemical techniques.

Yet illustratively, a combination of 2 or more distinct biologicalmarkers which may be quantified at step a) of the in vitro prognosismethod of the invention may be selected 2 or more biological markersthat are selected from the group consisting of CCR5, CR7, CD103, CD119,CD120a, CD120b, CD122, CD127, CD134, CD14, CD152, CD154, CD178, CD183,CD184, CD19, CD1α, CD210, CD25, CD26, CD27, CD28, CD3, CD32, CD4, CD44,CD45, CD45Ra, CD45Ro, CD47, CD49d, CD5, CD54, CD56, CD62L, CD69, CD7,CD8, CD80, CD83, CD86, CD95, CD97, CD98, CXCR6, GITR, HLA-DR, ICOS,IFNγRII, IL-18Rα, KIR-NKAT2, PD1, TCRαβ and TGFRII. Quantification ofthis group of biological markers is preferably performed, at step a) ofthe in vitro prognosis method of the invention, with immunohistochemicaltechniques. A list of antibodies directed specifically against each ofthese marker proteins is contained in Table 3 herein.

Still further, combinations of at least two biological markers encompasscombinations of two or more distinct biological markers selected fromthe group of biological comprising the following biological markers:T-box transcription factor 21 (T-bet), interferon regulatory factor 1(IRF-1), IFNγ, CD3ζ, CD8, granulysin (GLNY) and granzyme B (GZMB).Quantification of this group of biological markers is preferablyperformed, at step a) of the in vitro prognosis method of the invention,with immunohistochemical techniques.

Illustratively, the combination of two biological markers may beselected from the group consisting of CD8A-TBX21, CD3Z-CD8A, CD3Z-TBX21,B7H3-TGFB1, IFNG-TBX21, CD4-CD8A, CD8A, IFNG, CD4-TBX21, CD3Z-CD4,CD4-TGFB1, CD8A-GLNY, IFNG-IRF1, GLNY-IFNG, IRF1-TBX21, IL8-PTGS2,GLNY-TBX21, CD3Z-GLNY, CD3Z-IFNG, GZMB-IFNG, GLNY-IRF1, IL10-TGFB1,CD8A-IL10, CD4-IL10, CD8A-GZMB, GZMB-TBX21, CD3Z-GZMB, CD4-IRF1,GNLY-GZMB, B7H3-IL10, CD4-GZMB, GZMB-IRF1, IL10-TBX21, CD4-IFNG,B7H3-CD4, CD8A-TGFB1, CD3Z-IL10 and CD4-GNLY. Quantification of thisgroup of biological markers is preferably performed, at step a) of thein vitro prognosis method of the invention, with gene expressionanalysis techniques.

Other combinations of two biological markers that may be used,optionally with one or more distinct biological markers, are listed inTable 4 herein. Quantification of this group of biological markers ispreferably performed, at step a) of the in vitro prognosis method of theinvention, with gene expression analysis methods.

Further combinations of at least two markers that may be used,optionally with one or more distinct biological markers, are listed inTable 8 herein. Quantification of this group of biological markers ispreferably performed, at step a) of the in vitro prognosis method of theinvention, with gene expression analysis methods.

Still further, combinations of at least two biological markers encompasscombinations of two or more distinct biological markers selected fromthe group of biological markers that are listed in Table 9 herein,comprising the following biological markers: 18s, ACE, ACTB, AGTR1,AGTR2, APC, APOA1, ARF1, AXIN1, BAX, BCL2, BCL2L1, CXCR5, BMP2, BRCA1,BTLA, C3, CASP3, CASP9, CCL1, CCL11, CCL13, CCL16, CCL17, CCL18, CCL19,CCL2, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28,CCL3, CCL5, CCL7, CCL8, CCNB1, CCND1, CCNE1, CCR1, CCR10, CCR2, CCR3,CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCRL2, CD154, CD19, CD1a, CD2,CD226, CD244, PDCD1LG1, CD28, CD34, CD36, CD38, CD3E, CD3G, CD3Z, CD4,CD40LG, CD5, CD54, CD6, CD68, CD69, CLIP, CD80, CD83, SLAMF5, CD86,CD8A, CDH1, CDH7, CDK2, CDK4, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CEACAM1,COL4A5, CREBBP, CRLF2, CSF1, CSF2, CSF3, CTLA4, CTNNB1, CTSC, CX3CL1,CX3CR1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, CXCL2,CXCL3, CXCL5, CXCL6, CXCL9, CXCR3, CXCR4, CXCR6, CYP1A2, CYP7A1, DCC,DCN, DEFA6, DICER1, DKK1, Dok-1, Dok-2, DOK6, DVL1, E2F4, EBI3, ECE1,ECGF1, EDN1, EGF, EGFR, EIF4E, CD105, ENPEP, ERBB2, EREG, FCGR3A, CGR3B,FN1, FOXP3, FYN, FZD1, GAPD, GLI2, GNLY, GOLPH4, GRB2, GSK3B, GSTP1,GUSB, GZMA, GZMB, GZMH, GZMK, HLA-B, HLA-C, HLA-, MA, HLA-DMB, HLA-DOA,HLA-DOB, HLA-DPA1, HLA-DQA2, HLA-DRA, HLX1, HMOX1, HRAS, HSPB3, HUWE1,ICAM1, ICAM-2, ICOS, ID1, ifna1, ifna17, ifna2, ifna5, ifna6, ifna8,IFNAR1, IFNAR2, IFNG, IFNGR1, IFNGR2, IGF1, IHH, IKBKB, IL10, IL12A,IL12B, IL12RB1, IL12RB2, IL13, IL13RA2, IL15, IL15RA, IL17, IL17R,IL17RB, IL18, IL1A, IL1B, IL1R1, IL2, IL21, IL21R, IL23A, IL23R, IL24,IL27, IL2RA, IL2RB, IL2RG, IL3, IL31RA, IL4, IL4RA, IL5, IL6, IL7,IL7RA, IL8, CXCR1, CXCR2, IL9, IL9R, IRF1, ISGF3G, ITGA4, ITGA7,integrin, alpha E (antigen CD103, human mucosal lymphocyte, antigen 1;alpha polypeptide), Gene hCG33203, ITGB3, JAK2, JAK3, KLRB1, KLRC4,KLRF1, KLRG1, KRAS, LAG3, LAIR2, LEF1, LGALS9, LILRB3, LRP2, LTA,SLAMF3, MADCAM1, MADH3, MADH7, MAF, MAP2K1, MDM2, MICA, MICB, MK167,MMP12, MMP9, MTA1, MTSS1, MYC, MYD88, MYH6, NCAM1, NFATC1, NKG7, NLK,NOS2A, P2X7, PDCD1, PECAM-, CXCL4, PGK1, PIAS1, PIAS2, PIAS3, PIAS4,PLAT, PML, PP1A, CXCL7, PPP2CA, PRF1, PROM1, PSMB5, PTCH, PTGS2, PTP4A3,PTPN6, PTPRC, RAB23, RAC/RHO, RAC2, RAF, RB1, RBL1, REN, Drosha, SELE,SELL, SELP, SERPINE1, SFRP1, SIRP beta 1, SKI, SLAMF1, SLAMF6, SLAMF7,SLAMF8, SMAD2, SMAD4, SMO, SMOH, SMURF1, SOCS1, SOCS2, SOCS3, SOCS4,SOCS5, SOCS6, SOCS7, SOD1, SOD2, SOD3, SOS1, SOX17, CD43, ST14, STAM,STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, STAT6, STK36, TAP1, TAP2,TBX21, TCF7, TERT, TFRC, TGFA, TGFB1, TGFBR1, TGFBR2, TIMP3, TLR1,TLR10, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, TNF, TNFRSF10A,TNFRSF11A, TNFRSF18, TNFRSF1A, TNFRSF1B, OX-40, TNFRSF5, TNFRSF6,TNFRSF7, TNFRSF8, TNFRSF9, TNFSF10, TNFSF6, TOB1, TP53, TSLP, VCAM1,VEGF, WIF1, WNT1, WNT4, XCL1, XCR1, ZAP70 and ZIC2. Quantification ofthis group of biological markers is preferably performed, at step a) ofthe in vitro prognosis method of the invention, with gene expressionanalysis methods.

Yet further preferred combinations of at least two biological markersencompass combinations of two or more distinct biological markersselected from the group comprising the following biological markers:TNFRSF6B, CEACAM1, PDCD1LG1, CD8A, PTGS2, BIRC5, SELL, INDO, IRAK4, TNF,TNFRSF10A, MMP7, LILRB3, CD3Z, TNFRSF8, GAPD, CXCL10, EBAG9, IL8, STAT1,CXCR3, TGFB1, ICOS, CXCL9, CD97, IL18RAP, CXCR6, ART1, IRF1, B7H3, ACE,IL18R1, TBX21, IL18, PDCD1, IFNG, GNLY, GATA3, VEGF, GZMB, LAT, CD4,IRTA2, IL10, TNFSF4, THSD1 and PDCD1LG2. Quantification of this group ofbiological markers is preferably performed, at step a) of the in vitroprognosis method of the invention, with gene expression analysismethods.

Any combination of at least two biological markers selected from thegroup of biological markers that are described in the presentspecification are herein encompassed by the invention.

In certain embodiments of the method, a combination of two biologicalmarkers is used at step a), that may be also termed herein a “set” ofbiological markers.

A specific set of biological markers, that may be quantified throughgene expression analysis techniques, consists of the set consisting ofthe following biological markers: PDCD1LG1, VEGF, TNFRSF6B, IRF1, IL8RAand SELL. The said set of biological markers is of a high statisticalrelevance, as disclosed in the examples herein.

General Methods for Quantifying Biological Markers

Any one of the methods known by the one skilled in the art forquantifying cellular types, a protein-type or an nucleic acid-typebiological marker encompassed herein may be used for performing thecancer prognosis method of the invention. Thus any one of the standardand non-standard (emerging) techniques well known in the art fordetecting and quantifying a protein or a nucleic acid in a sample canreadily be applied.

Such techniques include detection and quantification of nucleicacid-type biological markers with nucleic probes or primers.

Such techniques also include detection and quantification ofprotein-type biological markers with any type of ligand molecule thatspecifically binds thereto, including nucleic acids (e.g. nucleic acidsselected for binding through the well known Selex method), andantibodies including antibody fragments. In certain embodiments whereinthe biological marker of interest consists of an enzyme, these detectionand quantification methods may also include detection and quantificationof the corresponding enzyme activity.

Noticeably, antibodies are presently already available for most, if notall, the biological markers described in the present specification,including those biological markers that are listed in Table 2.

Further, in situations wherein no antibody is yet available for a givenbiological marker, or in situations wherein the production of furtherantibodies to a given biological marker is sought, then antibodies tosaid given biological markers may be easily obtained with theconventional techniques, including generation of antibody-producinghybridomas. In this method, a protein or peptide comprising the entiretyor a segment of a biological marker protein is synthesized or isolated(e.g. by purification from a cell in which it is expressed or bytranscription and translation of a nucleic acid encoding the protein orpeptide in vivo or in vitro using known methods). A vertebrate,preferably a mammal such as a mouse, rat, rabbit, or sheep, is immunizedusing the protein or peptide. The vertebrate may optionally (andpreferably) be immunized at least one additional time with the proteinor peptide, so that the vertebrate exhibits a robust immune response tothe protein or peptide. Splenocytes are isolated from the immunizedvertebrate and fused with an immortalized cell line to form hybridomas,using any of a variety of methods well known in the art. Hybridomasformed in this manner are then screened using standard methods toidentify one or more hybridomas which produce an antibody whichspecifically binds with the biological marker protein or a fragmentthereof. The invention also encompasses hybridomas made by this methodand antibodies made using such hybridomas. Polyclonal antibodies may beused as well.

Expression of a biological marker of the invention may be assessed byany of a wide variety of well known methods for detecting expression ofa transcribed nucleic acid or protein. Non-limiting examples of suchmethods include immunological methods for detection of secreted,cell-surface, cytoplasmic, or nuclear proteins, protein purificationmethods, protein function or activity assays, nucleic acid hybridizationmethods, nucleic acid reverse transcription methods, and nucleic acidamplification methods.

In one preferred embodiment, expression of a marker is assessed using anantibody (e.g. a radio-labeled, chromophore-labeled,fluorophore-labeled, polymer-backbone-antibody, or enzyme-labeledantibody), an antibody derivative (e.g. an antibody conjugated with asubstrate or with the protein or ligand of a protein-ligand pair {e.g.biotin-streptavidin}), or an antibody fragment (e.g. a single-chainantibody, an isolated antibody hypervariable domain, etc.) which bindsspecifically with a marker protein or fragment thereof, including amarker protein which has undergone all or a portion of its normalpost-translational modification.

In another preferred embodiment, expression of a marker is assessed bypreparing mRNA/cDNA (i.e. a transcribed polynucleotide) from cells in apatient tumor tissue sample, and by hybridizing the mRNA/cDNA with areference polynucleotide which is a complement of a marker nucleic acid,or a fragment thereof. cDNA can, optionally, be amplified using any of avariety of polymerase chain reaction methods prior to hybridization withthe reference polynucleotide; preferably, it is not amplified.

In a related embodiment, a mixture of transcribed polynucleotidesobtained from the sample is contacted with a substrate having fixedthereto a polynucleotide complementary to or homologous with at least aportion (e.g. at least 7, 10, 15, 20, 25, 30, 40, 50, 100, 500, or morenucleotide residues) of a biological marker nucleic acid. Ifpolynucleotides complementary to or homologous with are differentiallydetectable on the substrate (e.g. detectable using differentchromophores or fluorophores, or fixed to different selected positions),then the levels of expression of a plurality of markers can be assessedsimultaneously using a single substrate (e.g. a “gene chip” microarrayof polynucleotides fixed at selected positions). When a method ofassessing marker expression is used which involves hybridization of onenucleic acid with another, it is preferred that the hybridization beperformed under stringent hybridization conditions.

An exemplary method for detecting and/or quantifying a biological markerprotein or nucleic acid in a tumor tissue sample involves obtaining atumor tissue sample (e.g. (i) a global primary tumor (as a whole), (ii)a tissue sample from the center of the tumor, (iii) a tissue sample fromthe tissue directly surrounding the tumor which tissue may be morespecifically named the “invasive margin” of the tumor, (iv) the lymphnodes located at the closest proximity of the tumor, (v) a tumor biopsieperform prior surgery (for follow-up of patients after treatment forexample), and (vi) a distant metastasis, from a cancer patient. Saidmethod includes further steps of contacting the biological sample with acompound or an agent capable of detecting the polypeptide or nucleicacid (e.g., mRNA, genomic DNA, or cDNA). The detection methods of theinvention can thus be used to detect mRNA, protein, cDNA, or genomicDNA, for example, in a tumor tissue sample in vitro. For example, invitro techniques for detection of mRNA include Northern hybridizationsand in situ hybridizations. In vitro techniques for detection of abiological marker protein include enzyme linked immunosorbent assays(ELISAs), Western blots, immunoprecipitations and immunofluorescence.Furthermore, in vivo techniques for detection of a marker proteininclude introducing into a subject a labeled antibody directed againstthe protein or fragment thereof. For example, the antibody can belabeled with a radioactive marker whose presence and location in asubject can be detected by standard imaging techniques.

A general principle of such detection and/or quantification assaysinvolves preparing a sample or reaction mixture that may contain abiological marker, and a probe, under appropriate conditions and for atime sufficient to allow the marker and probe to interact and bind, thusforming a complex that can be removed and/or detected in the reactionmixture.

As used herein, the term “probe” refers to any molecule which is capableof selectively binding to a specifically intended target molecule, forexample, a nucleotide transcript or protein encoded by or correspondingto a biological marker. Probes can be either synthesized by one skilledin the art, or derived from appropriate biological preparations. Forpurposes of detection of the target molecule, probes may be specificallydesigned to be labeled, as described herein. Examples of molecules thatcan be utilized as probes include, but are not limited to, RNA, DNA,proteins, antibodies, and organic molecules.

These detection and/or quantification assays of a biological marker canbe conducted in a variety of ways.

For example, one method to conduct such an assay would involve anchoringthe probe onto a solid phase support, also referred to as a substrate,and detecting target marker/probe complexes anchored on the solid phaseat the end of the reaction. In one embodiment of such a method, a samplefrom a subject, which is to be assayed for quantification of thebiological marker, can be anchored onto a carrier or solid phasesupport. In another embodiment, the reverse situation is possible, inwhich the probe can be anchored to a solid phase and a sample from asubject can be allowed to react as an unanchored component of the assay.

There are many established methods for anchoring assay components to asolid phase. These include, without limitation, marker or probemolecules which are immobilized through conjugation of biotin andstreptavidin. Such biotinylated assay components can be prepared frombiotin-NHS (N-hydroxy-succinimide) using techniques known in the art(e.g., biotinylation kit, Pierce Chemicals, Rockford, Ill.), andimmobilized in the wells of streptavidin-coated 96 well plates (PierceChemical). In certain embodiments, the surfaces with immobilized assaycomponents can be prepared in advance and stored.

Other suitable carriers or solid phase supports for such assays includeany material capable of binding the class of molecule to which themarker or probe belongs. Well-known supports or carriers include, butare not limited to, glass, polystyrene, nylon, polypropylene, nylon,polyethylene, dextran, amylases, natural and modified celluloses,polyacrylamides, gabbros, and magnetite.

In order to conduct assays with the above mentioned approaches, thenon-immobilized component is added to the solid phase upon which thesecond component is anchored. After the reaction is complete,uncomplexed components may be removed (e.g., by washing) underconditions such that any complexes formed will remain immobilized uponthe solid phase. The detection of marker/probe complexes anchored to thesolid phase can be accomplished in a number of methods outlined herein.

In a preferred embodiment, the probe, when it is the unanchored assaycomponent, can be labeled for the purpose of detection and readout ofthe assay, either directly or indirectly, with detectable labelsdiscussed herein and which are well-known to one skilled in the art.

It is also possible to directly detect marker/probe complex formationwithout further manipulation or labeling of either component (marker orprobe), for example by utilizing the technique of fluorescence energytransfer (see, for example, Lakowicz et al., U.S. Pat. No. 5,631,169;Stavrianopoulos, et al., U.S. Pat. No. 4,868,103). A fluorophore labelon the first, ‘donor’ molecule is selected such that, upon excitationwith incident light of appropriate wavelength, its emitted fluorescentenergy will be absorbed by a fluorescent label on a second ‘acceptor’molecule, which in turn is able to fluoresce due to the absorbed energy.Alternately, the ‘donor’ protein molecule may simply utilize the naturalfluorescent energy of tryptophan residues. Labels are chosen that emitdifferent wavelengths of light, such that the ‘acceptor’ molecule labelmay be differentiated from that of the ‘donor’. Since the efficiency ofenergy transfer between the labels is related to the distance separatingthe molecules, spatial relationships between the molecules can beassessed. In a situation in which binding occurs between the molecules,the fluorescent emission of the ‘acceptor’ molecule label in the assayshould be maximal. A FRET binding event can be conveniently measuredthrough standard fluorometric detection means well known in the art(e.g., using a fluorimeter).

In another embodiment, determination of the ability of a probe torecognize a marker can be accomplished without labeling either assaycomponent (probe or marker) by utilizing a technology such as real-timeBiomolecular Interaction Analysis (BIA) (see, e.g., Sjolander, S. andUrbaniczky, C., 1991, Anal. Chem. 63:2338-2345 and Szabo et al., 1995,Curr. Opin. Struct. Biol. 5:699-705). As used herein, “BIA” or “surfaceplasmon resonance” is a technology for studying biospecific interactionsin real time, without labeling any of the interactants (e.g., BIAcore).Changes in the mass at the binding surface (indicative of a bindingevent) result in alterations of the refractive index of light near thesurface (the optical phenomenon of surface plasmon resonance (SPR)),resulting in a detectable signal which can be used as an indication ofreal-time reactions between biological molecules.

Alternatively, in another embodiment, analogous diagnostic andprognostic assays can be conducted with marker and probe as solutes in aliquid phase. In such an assay, the complexed marker and probe areseparated from uncomplexed components by any of a number of standardtechniques, including but not limited to: differential centrifugation,chromatography, electrophoresis and immunoprecipitation. In differentialcentrifugation, marker/probe complexes may be separated from uncomplexedassay components through a series of centrifugal steps, due to thedifferent sedimentation equilibria of complexes based on their differentsizes and densities (see, for example, Rivas, G., and Minton, A. P.,1993, Trends Biochem Sci. 18(8):284-7). Standard chromatographictechniques may also be utilized to separate complexed molecules fromuncomplexed ones. For example, gel filtration chromatography separatesmolecules based on size, and through the utilization of an appropriategel filtration resin in a column format, for example, the relativelylarger complex may be separated from the relatively smaller uncomplexedcomponents. Similarly, the relatively different charge properties of themarker/probe complex as compared to the uncomplexed components may beexploited to differentiate the complex from uncomplexed components, forexample through the utilization of ion-exchange chromatography resins.Such resins and chromatographic techniques are well known to one skilledin the art (see, e.g., Heegaard, N. H., 1998, J. Mol. Recognit. Winter11(1-6):141-8; Hage, D. S., and Tweed, S. A. J Chromatogr B Biomed SciAppl 1997 Oct. 10; 699(1-2):499-525). Gel electrophoresis may also beemployed to separate complexed assay components from unbound components(see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology,John Wiley & Sons, New York, 1987-1999). In this technique, protein ornucleic acid complexes are separated based on size or charge, forexample. In order to maintain the binding interaction during theelectrophoretic process, non-denaturing gel matrix materials andconditions in the absence of reducing agent are typically preferred.SELDI-TOF technique may also be employed on matrix or beads coupled withactive surface, or not, or antibody coated surface, or beads.

Appropriate conditions to the particular assay and components thereofwill be well known to one skilled in the art.

In a particular embodiment, the level of marker mRNA can be determinedboth by in situ and by in vitro formats in a biological sample usingmethods known in the art. The term “biological sample” is intended toinclude tissues, cells, biological fluids and isolates thereof, isolatedfrom a subject, as well as tissues, cells and fluids present within asubject. Many expression detection methods use isolated RNA. For invitro methods, any RNA isolation technique that does not select againstthe isolation of mRNA can be utilized for the purification of RNA fromcolorectal cancer (see, e.g., Ausubel et al., ed., Current Protocols inMolecular Biology, John Wiley & Sons, New York 1987-1999). Additionally,large numbers of tissue samples can readily be processed usingtechniques well known to those of skill in the art, such as, forexample, the single-step RNA isolation process of Chomczynski (1989,U.S. Pat. No. 4,843,155).

The isolated mRNA can be used in hybridization or amplification assaysthat include, but are not limited to, Southern or Northern analyses,polymerase chain reaction analyses and probe arrays. One preferreddiagnostic method for the detection of mRNA levels involves contactingthe isolated mRNA with a nucleic acid molecule (probe) that canhybridize to the mRNA encoded by the gene being detected. The nucleicacid probe can be, for example, a full-length cDNA, or a portionthereof, such as an oligonucleotide of at least 7, 15, 30, 50, 100, 250or 500 nucleotides in length and sufficient to specifically hybridizeunder stringent conditions to a mRNA or genomic DNA encoding a marker ofthe present invention. Other suitable probes for use in the prognosticassays of the invention are described herein. Hybridization of an mRNAwith the probe indicates that the marker in question is being expressed.

In one format, the mRNA is immobilized on a solid surface and contactedwith a probe, for example by running the isolated mRNA on an agarose geland transferring the mRNA from the gel to a membrane, such asnitrocellulose. In an alternative format, the probe(s) are immobilizedon a solid surface and the mRNA is contacted with the probe(s), forexample, in an Affymetrix gene chip array. A skilled artisan can readilyadapt known mRNA detection methods for use in detecting the level ofmRNA encoded by the markers of the present invention.

An alternative method for determining the level of mRNA marker in asample involves the process of nucleic acid amplification, e.g., byrtPCR (the experimental embodiment set forth in Mullis, 1987, U.S. Pat.No. 4,683,202), ligase chain reaction (Barany, 1991, Proc. Natl. Acad.Sci. USA, 88:189-193), self sustained sequence replication (Guatelli etal., 1990, Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptionalamplification system (Kwoh et al., 1989, Proc. Natl. Acad. Sci. USA86:1173-1177), Q-Beta Replicase (Lizardi et al., 1988, Bio/Technology6:1197), rolling circle replication (Lizardi et al., U.S. Pat. No.5,854,033) or any other nucleic acid amplification method, followed bythe detection of the amplified molecules using techniques well known tothose of skill in the art. These detection schemes are especially usefulfor the detection of nucleic acid molecules if such molecules arepresent in very low numbers. As used herein, amplification primers aredefined as being a pair of nucleic acid molecules that can anneal to 5′or 3′ regions of a gene (plus and minus strands, respectively, orvice-versa) and contain a short region in between. In general,amplification primers are from about 10 to 30 nucleotides in length andflank a region from about 50 to 200 nucleotides in length. Underappropriate conditions and with appropriate reagents, such primerspermit the amplification of a nucleic acid molecule comprising thenucleotide sequence flanked by the primers.

For in situ methods, mRNA does not need to be isolated from thecolorectal cancer prior to detection. In such methods, a cell or tissuesample is prepared/processed using known histological methods. Thesample is then immobilized on a support, typically a glass slide, andthen contacted with a probe that can hybridize to mRNA that encodes themarker.

As an alternative to making determinations based on the absoluteexpression level of the marker, determinations may be based on thenormalized expression level of the marker. Expression levels arenormalized by correcting the absolute expression level of a marker bycomparing its expression to the expression of a gene that is not amarker, e.g., a housekeeping gene that is constitutively expressed.Suitable genes for normalization include housekeeping genes such as theactin gene, ribosomal 18S gene, GAPD gene, or epithelial cell-specificgenes. This normalization allows the comparison of the expression levelin one sample, e.g., a patient sample, to another sample, e.g., anon-colorectal cancer sample, or between samples from different sources.

Alternatively, the expression level can be provided as a relativeexpression level. To determine a relative expression level of a marker,the level of expression of the marker is determined for 10 or moresamples of normal versus cancer cell isolates, preferably 50 or moresamples, prior to the determination of the expression level for thesample in question. The mean expression level of each of the genesassayed in the larger number of samples is determined and this is usedas a baseline expression level for the marker. The expression level ofthe marker determined for the test sample (absolute level of expression)is then divided by the mean expression value obtained for that marker.This provides a relative expression level.

As already mentioned previously in the present specification, onepreferred agent for detecting and/or quantifying a biological markerprotein when performing the cancer prognosis method of the invention isan antibody that specifically bind to such a biological marker proteinor a fragment thereof, preferably an antibody with a detectable label.Antibodies can be polyclonal, or more preferably, monoclonal. An intactantibody, or a fragment or derivative thereof (e.g., Fab orF(ab′).sub.2) can be used. The term “labeled”, with regard to the probeor antibody, is intended to encompass direct labeling of the probe orantibody by coupling (i.e., physically linking) a detectable substanceto the probe or antibody, as well as indirect labeling of the probe orantibody by reactivity with another reagent that is directly labeled.Examples of indirect labeling include detection of a primary antibodyusing a fluorescently labeled secondary antibody and end-labeling of aDNA probe with biotin such that it can be detected with fluorescentlylabeled streptavidin.

A variety of formats can be employed to determine whether a samplecontains a biological marker protein that binds to a given antibody.Examples of such formats include, but are not limited to, enzymeimmunoassay (EIA), radioimmunoassay (RIA), Western blot analysis andenzyme linked immunoabsorbant assay (ELISA). A skilled artisan canreadily adapt known protein/antibody detection and/or quantificationmethods for use in the cancer prognosis method according to theinvention.

In one format, antibodies, or antibody fragments or derivatives, can beused in methods such as Western blots, SELDI-TOF (carried out withantibody-beads coupled or matrix) or immunofluorescence techniques todetect the expressed proteins. In such uses, it is generally preferableto immobilize either the antibody or proteins on a solid support.Suitable solid phase supports or carriers include any support capable ofbinding an antigen or an antibody. Well-known supports or carriersinclude glass, polystyrene, polypropylene, polyethylene, dextran, nylon,amylases, natural and modified celluloses, polyacrylamides, gabbros, andmagnetite.

One skilled in the art will know many other suitable carriers forbinding antibody or antigen, and will be able to adapt such support foruse with the present invention. For example, protein isolated fromcolorectal cancer can be run on a polyacrylamide gel electrophoresis andimmobilized onto a solid phase support such as nitrocellulose. Thesupport can then be washed with suitable buffers followed by treatmentwith the detectably labeled antibody. The solid phase support can thenbe washed with the buffer a second time to remove unbound antibody. Theamount of bound label on the solid support can then be detected byconventional means.

The most preferred methods for quantifying a biological marker for thepurpose of carrying out the cancer prognosis method of the invention aredescribed hereunder.

Quantifying Biological Markers by Tissue Microaray

In certain embodiments, a biological marker, or a set of biologicalmarkers, may be quantified with any one of the Tissue Microarray methodsknown in the art.

Tissue microarrays are produced by a method of re-locating tissue fromconventional histologic paraffin blocks such that tissue from multiplepatients or blocks can be seen on the same slide. This is done by usinga needle to biopsy a standard histologic sections and placing the coreinto an array on a recipient paraffin block. This technique wasoriginally described by Wan in 1987 (Wan, Fortuna and Furmanski; Journalof Immunological Methods). In certain embodiments of Tissue microarrays,tissue cores are placed in specific spatially fixed positions in ablock. The technique is disclosed notably by Kononen et al. (NatureMedicine in 1998).

The tissue array technique involves acquiring small, minimal cylindricalsamples from paraffin embedded tissue specimens. These cylinders arethen arrayed in a systematic, high-density grid into another paraffinblock.

For instance, tumor tissue samples, including under the form of biopsysamples (i) of the center of the tumor, (ii) of the invasion margin or(iii) of the regional lymph nodes, are obtained from an appropriatenumber of individuals, formalin-fixed, paraffin-embedded tumor tissueblocks. These are transferred to a TMA block. Multiple TMA blocks can begenerated at the same time. Each TMA block can be sectioned up to 300times, with all resulting TMA slides having the same tissues in the samecoordinate positions. The individual slides can be used for a variety ofmolecular analyses, such as H&E staining to ascertain tissue morphology,mRNA in situ hybridization, protein immunohistochemistry, or analysis ofDNA for genetic alteration.

Because these cylindrical tumor tissue samples are small (0.4-1 mmdiameter×3-4 mm in height), up to a thousand tissues can be arrayed in asingle paraffin block while minimizing the damage and tissuerequirement. Furthermore, these paraffin blocks can be transversely cutinto hundreds of tissue microarray sections, which can then each be usedfor different genetic analyses.

In addition to the increased speed of analyses, tissue microarrays mayalso ensure the reproducibility and reliability of the cancer prognosismethod according to the invention, because the hundreds of differenttissue samples are handled, prepared, and stained in a parallel,virtually identical manner all on the same slide (Kallioniemi, O.;Wagner, U.; Kononen, J. and Sauter, G. Tissue microarray technology forhigh-throughput molecular profiling of cancer. Human Molecular Genetics(2001), 10, 657-662.).

Typically, representative areas of the tumor are removed fromparaffin-embedded tumor tissue blocks, whereby tumor tissue samples areobtained. Then, said tumor tissue samples are transferred to anotherrecipient paraffin block where these samples are spotted. Then, thetissue sample spots that are arrayed in said recipient paraffin blockare cut into thin sections, typically 2-5 μm sections, for furtheranalysis.

Typically, for further analysis, one thin section of the array, namelythe Tissue Microarray, is firstly incubated with labeled antibodiesdirected against one biological marker of interest. After washing, thelabeled antibodies that are bound to said biological marker of interestare revealed by the appropriate technique, depending of the kind oflabel is borne by the labeled antibody, e.g. radioactive, fluorescent orenzyme label. Multiple labelling can also be performed simultaneously,especially in embodiments wherein more than one protein-specificantibody is used, for the purpose of quantifying more than onebiological marker.

Illustrative embodiments of quantification of biological markers usingTissue Microarrays are disclosed in the examples herein.

Quantifying Biological Markers by Immunohistochemistry on ConventionalTissue Slides (Paraffin-Embedded or Frozen Specimens)

In certain embodiments, a biological marker, or a set of biologicalmarkers, may be quantified with any one of the immunohistochemistrymethods known in the art.

Analysis can then performed on (i) a global primary tumor (as a whole),(ii) a tissue sample from the center of the tumor, (iii) a tissue samplefrom the tissue directly surrounding the tumor which tissue may be morespecifically named the “invasive margin” of the tumor and (iv) the lymphnodes located at the closest proximity of the tumor, (vi) a distantmetastasis

Analysis can also, and preferably, be performed in combined tumorregions (defined above).

Typically, for further analysis, one thin section of the tumor, isfirstly incubated with labeled antibodies directed against onebiological marker of interest. After washing, the labeled antibodiesthat are bound to said biological marker of interest are revealed by theappropriate technique, depending of the kind of label is borne by thelabeled antibody, e.g. radioactive, fluorescent or enzyme label.Multiple labelling can be performed simultaneously.

Quantifying Biological Markers by Flow Cytometry Methods

In certain embodiments, a biological marker, or a set of biologicalmarkers, may be quantified with any one of the flow cytometry methodsknown in the art.

For example, cells contained in the tumor tissue sample being tested arefirstly extracted by mechanical dispersion and cell suspensions inliquid medium are prepared.

Then, the thus obtained cells are incubated during the appropriate timeperiod with antibodies specifically directed against the biologicalmarker(s) that is (are) to be quantified.

After washing the cell suspension in order to remove the unboundantibodies, the resulting cells are analyzed by performing flowcytometry, in view of quantifying the percentage of the total number ofcells present in the cell suspension that express each of saidbiological marker(s).

Illustrative embodiments of quantification biological markers using flowcytometry methods are disclosed in the examples herein.

Quantifying Biological Markers by Nucleic Acid Amplification

In certain embodiments, a biological marker, or a set of biologicalmarkers, may be quantified with any one of the nucleic acidamplification methods known in the art.

Analysis can then performed on (i) a global primary tumor (as a whole),(ii) a tissue sample from the center of the tumor (CT), (iii) a tissuesample from the tissue directly surrounding the tumor which tissue maybe more specifically named the “invasive margin” of the tumor, (iv) thelymph nodes located at the closest proximity of the tumor, and (vi) adistant metastasis

Analysis can also, and preferably, be performed in combined tumorregions (defined above) after tumor microdissection.

The polymerase chain reaction (PCR) is a highly sensitive- and powerfulmethod for such biological markers quantification

For performing any one of the nucleic acid amplification method that isappropriate for quantifying a biological marker when performing thecancer prognosis method of the invention, a pair of primers thatspecifically hybridize with the target mRNA or with the target cDNA isrequired.

A pair of primers that specifically hybridise with the target nucleicacid biological marker of interest may be designed by any one of thenumerous methods known in the art.

In certain embodiments, for each of the biological markers of theinvention, at least one pair of specific primers, as well as thecorresponding detection nucleic acid probe, is already referenced andentirely described in the public “Quantitative PCR primer database”,notably at the following Internet address:http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.

In other embodiments, a specific pair of primers may be designed usingthe method disclosed in the U.S. Pat. No. 6,892,141 to Nakae et al., theentire disclosure of which is herein incorporated by reference.

Many specific adaptations of the PCR technique are known in the art forboth qualitative and quantitative detections. In particular, methods areknown to utilize fluorescent dyes for detecting and quantifyingamplified PCR products. In situ amplification and detection, also knownas homogenous PCR, have also been previously described. See e.g. Higuchiet al., (Kinetics PCR Analysis: Real-time Monitoring of DNAAmplification Reactions, Bio/Technology, Vol 11, pp 1026-1030 (1993)),Ishiguro et al., (Homogeneous quantitative Assay of Hepatitis C VirusRNA by Polymerase Chain Reaction in the Presence of a FluorescentIntercalater, Anal. Biochemistry 229, pp 20-213 (1995)), and Wittwer etal., (Continuous Fluorescence Monitoring of Rapid cycle DNAAmplification, Biotechniques, vol. 22, pp 130-138 (1997.))

A number of other methods have also been developed to quantify nucleicacids (Southern, E. M., J. Mol. Biol., 98:503-517, 1975; Sharp, P. A.,et al., Methods Enzymol. 65:750-768, 1980; Thomas, P. S., Proc. Nat.Acad. Sci., 77:5201-5205, 1980). More recently, PCR and RT-PCR methodshave been developed which are capable of measuring the amount of anucleic acid in a sample. One approach, for example, measures PCRproduct quantity in the log phase of the reaction before the formationof reaction products plateaus (Kellogg, D. E., et al., Anal. Biochem.189:202-208 (1990); and Pang, S., et al., Nature 343:85-89 (1990)). Agene sequence contained in all samples at relatively constant quantityis typically utilized for sample amplification efficiency normalization.This approach, however, suffers from several drawbacks. The methodrequires that each sample have equal input amounts of the nucleic acidand that the amplification efficiency between samples be identical untilthe time of analysis. Furthermore, it is difficult using theconventional methods of PCR quantitation such as gel electrophoresis orplate capture hybridization to determine that all samples are in factanalyzed during the log phase of the reaction as required by the method.

Another method called quantitative competitive (QC)-PCR, as the nameimplies, relies on the inclusion of an internal control competitor ineach reaction (Becker-Andre, M., Meth. Mol. Cell Biol. 2:189-201 (1991);Piatak, M. J., et al., BioTechniques 14:70-81 (1993); and Piatak, M. J.,et al., Science 259:1749-1754 (1993)). The efficiency of each reactionis normalized to the internal competitor. A known amount of internalcompetitor is typically added to each sample. The unknown target PCRproduct is compared with the known competitor PCR product to obtainrelative quantitation. A difficulty with this general approach lies indeveloping an internal control that amplifies with the same efficiencyof the target molecule.

For instance, the nucleic acid amplification method that is used mayconsist of Real-Time quantitative PCR analysis.

Real-time or quantitative PCR (QPCR) allows quantification of startingamounts of DNA, cDNA, or RNA templates. QPCR is based on the detectionof a fluorescent reporter molecule that increases as PCR productaccumulates with each cycle of amplification. Fluorescent reportermolecules include dyes that bind double-stranded DNA (i.e. SYBR Green I)or sequence-specific probes (i.e. Molecular Beacons or TaqMan® Probes).

Preferred nucleic acid amplification methods are quantitative PCRamplification methods, including multiplex quantitative PCR method suchas the technique disclosed in the published US patent Application no US2005/0089862, to Therianos et al., the entire disclosure of which isherein incorporated by reference.

Illustratively, for quantifying biological markers of the invention,tumor tissue samples are snap-frozen shortly after biopsy collection.Then, total RNA from a “tumor tissue sample” (i) a global primary tumor(as a whole), (ii) a tissue sample from the center of the tumor, (iii) atissue sample from the tissue directly surrounding the tumor whichtissue may be more specifically named the “invasive margin” of thetumor, (iv) the lymph nodes located at the closest proximity of thetumor, (v) a tumor biopsie perform prior surgery (for follow-up ofpatients after treatment for example), and (vi) a distant metastasis, isisolated and quantified. Then, each sample of the extracted andquantified RNA is reverse-transcribed and the resulting cDNA isamplified by PCR, using a pair of specific primers for each biologicalmarker that is quantified. Control pair of primers are simultaneouslyused as controls, such as pair of primers that specifically hybridisewith 18S cDNA and GADPH cDNA, or any other well known “housekeeping”gene.

Illustrative embodiments of quantification biological markers usingnucleic acid amplification methods are disclosed in the examples herein.

Cancer Prognosis Kits

The invention includes a kit for assessing the prognosis of a cancer ina patient (e.g. in a sample such as a tumor tissue patient sample). Thekit comprises a plurality of reagents, each of which is capable ofbinding specifically with a biological marker nucleic acid or protein.Suitable reagents for binding with a marker protein include antibodies,antibody derivatives, antibody fragments, and the like. Suitablereagents for binding with a marker nucleic acid (e.g. a genomic DNA, anmRNA, a spliced mRNA, a cDNA, or the like) include complementary nucleicacids. For example, the nucleic acid reagents may includeoligonucleotides (labeled or non-labeled) fixed to a substrate, labeledoligonucleotides not bound with a substrate, pairs of PCR primers,molecular beacon probes, and the like.

Thus, a further object of this invention consists of a kit for theprognosis of progression of a cancer in a patient, which kit comprisesmeans for quantifying at least one biological marker indicative of thestatus of the adaptive immune response of said patient against cancer.

The kit of the invention may optionally comprise additional componentsuseful for performing the methods of the invention. By way of example,the kit may comprise fluids (e.g. SSC buffer) suitable for annealingcomplementary nucleic acids or for binding an antibody with a proteinwith which it specifically binds, one or more sample compartments, aninstructional material which describes performance of the cancerprognosis method of the invention, and the like.

Kits Comprising Antibodies

In certain embodiments, a kit according to the invention comprises oneor a combination or a set of antibodies, each kind of antibodies beingdirected specifically against one biological marker of the invention.

In one embodiment, said kit comprises a combination or a set ofantibodies comprising at least two kind of antibodies, each kind ofantibodies being selected from the group consisting of antibodiesdirected against one of the CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1,IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine,IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5,CD2 biological markers.

An antibody kit according to the invention may comprise 2 to 20 kinds ofantibodies, each kind of antibodies being directed specifically againstone biological marker of the invention. For instance, an antibody kitaccording to the invention may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19 or 20 kinds of antibodies, each kind ofantibodies being directed specifically against one biological marker asdefined herein.

Various antibodies directed against biological markers according to theinvention encompass antibodies directed against biological markersselected from the group consisting of CD3, CD8, GZMB, CD45RO, GLNY,TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta,Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ,LAT, ZAP70, CD5 and CD2.

Various antibodies directed against biological markers according to theinvention encompass antibodies directed against biological markersselected from the group consisting of CCR5, CR7, CD103, CD119, CD120a,CD120b, CD122, CD127, CD134, CD14, CD152, CD154, CD178, CD183, CD184,CD19, CD1a, CD210, CD25, CD26, CD27, CD28, CD3, CD32, CD4, CD44, CD45,CD45Ra, CD45Ro, CD47, CD49d, CD5, CD54, CD56, CD62L, CD69, CD7, CD8,CD80, CD83, CD86, CD95, CD97, CD98, CXCR6, GITR, HLA-DR, ICOS, IFNγRII,IL-18Ra, KIR-NKAT2, PD1, TCRαβ and TGFRII. Specific embodiments of theseantibodies are listed in Table 3 herein.

Biological markers detectable by specific antibodies may also beselected form the group of biological markers consisting of CD3, CD8,GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6,IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG,Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5, CD2.

In certain other embodiments, a kit according to the invention comprisesone or a combination or a set of pair of ligands or specific solublemolecules binding with one or more of the biological marker(s), of theinvention.

Kits Comprising Nucleic Acid Primers

In certain other embodiments, a kit according to the invention comprisesone or a combination or a set of pair of primers, each kind of pair ofprimers hybridising specifically with one biological marker of theinvention.

In one embodiment, said kit comprises a combination or a set of pair ofprimers comprising at least two kind of pair of primers, each kind ofpair of primers being selected from the group consisting of pair ofprimers hybridising with one of the CD8A-TBX21, CD3Z-CD8A, CD3Z-TBX21,B7H3-TGFB1, IFNG-TBX21, CD4-CD8A, CD8A, IFNG, CD4-TBX21, CD3Z-CD4,CD4-TGFB1, CD8A-GLNY, IFNG-IRF1, GLNY-IFNG, IRF1-TBX21, IL8-PTGS2,GLNY-TBX21, CD3Z-GLNY, CD3Z-IFNG, GZMB-IFNG, GLNY-IRF1, IL10-TGFB1,CD8A-IL10, CD4-IL10, CD8A-GZMB, GZMB-TBX21, CD3Z-GZMB, CD4-IRF1,GNLY-GZMB, B7H3-IL10, CD4-GZMB, GZMB-IRF1, IL10-TBX21, CD4-IFNG,B7H3-CD4, CD8A-TGFB1, CD3Z-IL10 and CD4-GNLY biological markers.

In another embodiment, said kit comprises a combination or a set of pairof primers comprising at least two kind of pair of primers, each kind ofpair of primers being selected from the group consisting of pair ofprimers hybridising with one of the 18s, ACE, ACTB, AGTR1, AGTR2, APC,APOA1, ARF1, AXIN1, BAX, BCL2, BCL2L1, CXCR5, BMP2, BRCA1, BTLA, C3,CASP3, CASP9, CCL1, CCL11, CCL13, CCL16, CCL17, CCL18, CCL19, CCL2,CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3,CCL5, CCL7, CCL8, CCNB1, CCND1, CCNE1, CCR1, CCR10, CCR2, CCR3, CCR4,CCR5, CCR6, CCR7, CCR8, CCR9, CCRL2, CD154, CD19, CD1a, CD2, CD226,CD244, PDCD1LG1, CD28, CD34, CD36, CD38, CD3E, CD3G, CD3Z, CD4, CD40LG,CD5, CD54, CD6, CD68, CD69, CLIP, CD80, CD83, SLAMF5, CD86, CD8A, CDH1,CDH7, CDK2, CDK4, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CEACAM1, COL4A5,CREBBP, CRLF2, CSF1, CSF2, CSF3, CTLA4, CTNNB1, CTSC, CX3CL1, CX3CR1,CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, CXCL2, CXCL3,CXCL5, CXCL6, CXCL9, CXCR3, CXCR4, CXCR6, CYP1A2, CYP7A1, DCC, DCN,DEFA6, DICER1, DKK1, Dok-1, Dok-2, DOK6, DVL1, E2F4, EBI3, ECE1, ECGF1,EDN1, EGF, EGFR, EIF4E, CD105, ENPEP, ERBB2, EREG, FCGR3A, CGR3B, FN1,FOXP3, FYN, FZD1, GAPD, GLI2, GNLY, GOLPH4, GRB2, GSK3B, GSTP1, GUSB,GZMA, GZMB, GZMH, GZMK, HLA-B, HLA-C, HLA-, MA, HLA-DMB, HLA-DOA,HLA-DOB, HLA-DPA1, HLA-DQA2, HLA-DRA, HLX1, HMOX1, HRAS, HSPB3, HUWE1,ICAM1, ICAM-2, ICOS, ID1, ifna1, ifna17, ifna2, ifna5, ifna6, ifna8,IFNAR1, IFNAR2, IFNG, IFNGR1, IFNGR2, IGF1, IHH, IKBKB, IL10, IL12A,IL12B, IL12RB1, IL12RB2, IL13, IL13RA2, IL15, IL15RA, IL17, IL17R,IL17RB, IL18, IL1A, IL1B, IL1R1, IL2, IL21, IL21R, IL23A, IL23R, IL24,IL27, IL2RA, IL2RB, IL2RG, IL3, IL31RA, IL4, IL4RA, IL5, IL6, IL7,IL7RA, IL8, CXCR1, CXCR2, IL9, IL9R, IRF1, ISGF3G, ITGA4, ITGA7,integrin, alpha E (antigen CD103, human mucosal lymphocyte, ntigen 1;alpha polypeptide), Gene hCG33203, ITGB3, JAK2, JAK3, KLRB1, KLRC4,KLRF1, KLRG1, KRAS, LAG3, LAIR2, LEF1, LGALS9, LILRB3, LRP2, LTA,SLAMF3, MADCAM1, MADH3, MADH7, MAF, MAP2K1, MDM2, MICA, MICB, MK167,MMP12, MMP9, MTA1, MTSS1, MYC, MYD88, MYH6, NCAM1, NFATC1, NKG7, NLK,NOS2A, P2X7, PDCD1, PECAM-, CXCL4, PGK1, PIAS1, PIAS2, PIAS3, PIAS4,PLAT, PML, PP1A, CXCL7, PPP2CA, PRF1, PROM1, PSMB5, PTCH, PTGS2, PTP4A3,PTPN6, PTPRC, RAB23, RAC/RHO, RAC2, RAF, RB1, RBL1, REN, Drosha, SELE,SELL, SELP, SERPINE1, SFRP1, SIRP beta 1, SKI, SLAMF1, SLAMF6, SLAMF7,SLAMF8, SMAD2, SMAD4, SMO, SMOH, SMURF1, SOCS1, SOCS2, SOCS3, SOCS4,SOCS5, SOCS6, SOCS7, SOD1, SOD2, SOD3, SOS1, SOX17, CD43, ST14, STAM,STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, STAT6, STK36, TAP1, TAP2,TBX21, TCF7, TERT, TFRC, TGFA, TGFB1, TGFBR1, TGFBR2, TIMP3, TLR1,TLR10, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, TNF, TNFRSF10A,TNFRSF11A, TNFRSF18, TNFRSF1A, TNFRSF1B, OX-40, TNFRSF5, TNFRSF6,TNFRSF7, TNFRSF8, TNFRSF9, TNFSF10, TNFSF6, TOB1, TP53, TSLP, VCAM1,VEGF, WIF1, WNT1, WNT4, XCL1, XCR1, ZAP70 and ZIC2 biological markers.

In one embodiment, said kit comprises a combination or a set of pair ofprimers comprising at least two kind of pair of primers, each kind ofpair of primers being selected from the group consisting of pair ofprimers hybridising with one of the TNFRSF6B, CEACAM1, PDCD1LG1, CD8A,PTGS2, BIRC5, SELL, INDO, IRAK4, TNF, TNFRSF10A, MMP7, LILRB3, CD3Z,TNFRSF8, GAPD, CXCL10, EBAG9, IL8, STAT1, CXCR3, TGFB1, ICOS, CXCL9,CD97, IL18RAP, CXCR6, ART1, IRF1, B7H3, ACE, IL18R1, TBX21, IL18, PDCD1,IFNG, GNLY, GATA3, VEGF, GZMB, LAT, CD4, IRTA2, IL10, TNFSF4, THSD1 andPDCD1 LG2 biological markers.

In one embodiment, said kit comprises a combination or a set of pair ofprimers comprising at least two kinds of pair of primers, each kind ofpair of primers being selected from the group consisting of pair ofprimers hybridising with one of the CD3, CD8, GZMB, CD45RO, GLNY, TBX21,IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta,Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ,LAT, ZAP70, CD5, CD2 biological markers.

In still another embodiment, said kit comprises a set or a combinationof nucleic acid primers, or pairs of primers, each primer or pair ofprimer hybridising with each of PDCD1LG1, VEGF, TNFRSF6B, IRF1, IL8RAand SELL biological markers.

A primer kit according to the invention may comprise 2 to 20 kinds ofpair or primers, each kind of pair of primers hybridising specificallywith one biological marker of the invention. For instance, a primer kitaccording to the invention may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19 or 20 kinds of pairs of primers, eachkind of pair of primers hybridising specifically against one biologicalmarker as defined herein.

Notably, at least one pair of specific primers, as well as thecorresponding detection nucleic acid probe, that hybridize specificallywith one biological marker of interest, is already referenced andentirely described in the public “Quantitative PCR primer database”,notably at the following Internet addresshttp://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.

Monitoring Anti-Cancer Treatments

Monitoring the influence of agents (e.g., drug compounds) on the levelof expression of a biological marker of the invention can be applied formonitoring the status of the adaptive immune response of the patientwith time. For example, the effectiveness of an agent to affectbiological marker expression can be monitored during treatments ofsubjects receiving anti-cancer treatments.

In a preferred embodiment, the present invention provides a method formonitoring the effectiveness of treatment of a subject with an agent(e.g., an agonist, antagonist, peptidomimetic, protein, peptide, nucleicacid, small molecule, or other drug candidate) comprising the steps of(i) obtaining a pre-administration sample from a subject prior toadministration of the agent; (ii) detecting the level of expression ofone or more selected biological markers of the invention in thepre-administration sample; (iii) obtaining one or morepost-administration samples from the subject; (iv) detecting the levelof expression of the biological marker(s) in the post-administrationsamples; (v) comparing the level of expression of the biologicalmarker(s) in the pre-administration sample with the level of expressionof the marker(s) in the post-administration sample or samples; and (vi)altering the administration of the agent to the subject accordingly. Forexample, decreased expression of the biological marker gene(s) duringthe course of treatment may indicate ineffective dosage and thedesirability of increasing the dosage. Conversely, increased expressionof the biological marker gene(s) may indicate efficacious treatment andno need to change dosage.

As already mentioned previously in the present specification, performingthe cancer prognosis method of the invention may indicate, with moreprecision than the prior art methods, those patients at high-risk oftumor recurrence who may benefit from adjuvant therapy, includingimmunotherapy.

For example, if, at the end of the cancer prognosis method of theinvention, a good prognosis of no metastasis or a prognosis of a longtime period of disease-free survival is determined, then the subsequentanti-cancer treatment will not comprise any adjuvant chemotherapy.

However, if, at the end of the cancer prognosis method of the invention,a bad prognosis with metastasis or a bad prognosis with a short timeperiod of disease-free survival is determined, then the patient isadministered with the appropriate composition of adjuvant chemotherapy.

Further, if, at the end of the cancer prognosis method of the invention,a bad prognosis with metastasis or a bad prognosis with a short timeperiod of disease-free survival is determined, then the patient isadministered with the appropriate immunostimulating composition,including pharmaceutical composition comprising an immunostimulatingcytokine or chemokine, including interleukines.

Preferred immunostimulating cytokines or chemokines are selected fromthe group consisting of IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-7,G-CSF, IL-15, GM-CSF, IFN-γ, CXCL9, CXCL10, Fractalkine, MIG, IFN,IL-18, IL-12, IL-23 and IL-31.

Accordingly, the present invention also relates to a method for adaptinga cancer treatment in a cancer patient, wherein said method comprisesthe steps of:

-   -   a) performing, on at least one tumor tissue sample collected        from said patient, the cancer prognosis method that is disclosed        herein;    -   b) adapting the cancer treatment of said cancer patient by        administering to said patient an adjuvant chemotherapy or an        immunostimulating therapy if a bad cancer prognosis is        determined at the end of step a).

Another object of the invention consists of a kit for monitoring theeffectiveness of treatment (adjuvant or neo-adjuvant) of a subject withan agent, which kit comprises means for quantifying at least onebiological marker indicative of the status of the adaptive immuneresponse of said patient against cancer.

The present invention is further illustrated by, without in any waybeing limited to, the examples below.

EXAMPLES

A. Material and Methods of the Examples

Material and Methods of Examples 1 to 4.

A.1. Patients and Database

All cases of colorectal cancer (n=959) who underwent a primary resectionof the tumor at the Laennec/HEGP Hospital between 1986 and 2004 werereviewed. The observation time in this unselected cohort was theinterval between diagnosis and last contact (death or last follow-up).Data were censored at the last follow-up for patients who had notrelapsed or who had died. The mean duration of follow-up was 44.5months. Six patients lost to follow-up were excluded from the analysis.Histopathological and clinical findings were scored according to theUICC-TNM staging system (Sobin L H, Wittekind C, (eds). UICC TNMclassification of malignant tumors. 6th ed. New York: Wiley-Liss, 2002),as disclosed in Table 1. Early metastatic invasion was defined as thepresence of vascular emboli (VE), lymphatic invasion (LI), or perineuralinvasion (PI). A VELIPI-positive tumor had at least one of thesepathologic findings, whereas a VELIPI-negative tumor had none of thethree findings. TNM and VELIPI status of the tumors were determined fromthe histopathological reports at the time of resection. A secureWeb-based database, TME.db (Tumoral MicroEnvironment Database), wasbuilt on a 3-tier architecture using Java-2 Enterprise-Edition (J2EE) tointegrate clinical data sets, and the results from high-throughputtechnologies.

The records of 959 Patients with colorectal who underwent a primaryresection at the Laennec/HEGP Hospital between 1986 and 2004 wereretrospectively reviewed representing a prospective, continuous,unselected cohort of patients. Conventional histopathologicalparameters, including AJCC/UICC TNM stage, Duke's stage, tumor type, andgrade of differentiation, lymphovascular embolies, perineural tumorinvasion (VELIPI), are detailed in Table 1. Data on adjuvant andpalliative chemotherapy were recorded. Adjuvant with 5-fluorouracil(FU)-based chemotherapy was administered to 327 patients (57 patientswith stage II disease, 136 with stage III disease, and 134 with stage IVdisease).

Postsurgery patient surveillance was carried-out, at Laennec/HEGP andassociated hospitals, for all patients according to general practice forcolon cancer patients, including physical examination, blood counts,liver function tests, serum carcinoembryonic antigen, abdominalultrasonography and computed tomography scan, and pulmonary x-ray.Colonoscopy was performed one year after resection and then once everythree years if normal. If tumor relapse was suspected, the patientunderwent intensive work-up, including abdominal computed tomographyscan, magnetic resonance imaging, chest x-ray, colonoscopy, and biopsy,when applicable.

Clinical findings, treatment, histopathologic report, and follow-up datawere collected prospectively and updated (by A.B) and included intoTME.db. The database is accessible upon request tozlatko.traanosk@tugraz.at. Observation time was the interval betweendiagnosis and last contact (death or last follow-up). Data were censoredat the last follow-up for patients without relapse, or death. The meanduration of follow-up was 44.5 months. The min:max values untilprogression/death or last follow-up were (0:214) months, respectively.Six patients lost to follow-up were excluded from the analysis. Time torecurrence or disease-free time was defined as the time period from thedate of surgery to confirmed tumor relapse date for relapsed patientsand from the date of surgery to the date of last follow-up fordisease-free patients.

A.2. Histopathology

All the H&E sections of the tumors for each patient were reassessedblindly by two pathologist (D.D., T.M.) or two investigators (F.P.,J.G.) trained in the pathology of colonic cancer, for each of thefollowing: (a) tumoral lymphoid infiltrate (b) lymphoid reaction at theinvasive margin (10 to 20 fields analyzed per-patient). The densities ofthese immune infiltrates were scored independently by the investigators,as weak (score 1), moderate (score 2), or strong (score 3), as disclosedhereunder.

Three hundred seventy seven randomly selected tumors from the 415 tumorsevaluated by TMA were reassessed for immune cell density. Review oftissue sections was performed independently by two pathologists (D.D.,T.M.) or two investigators (F.P. and J.G.) trained in the pathology ofcolonic cancer. A mean number of four sections of primary tumor wereanalyzed. The fields analyzed were chosen as representative of theregion, and were at distance from necrotic material or abscesses. Immuneinfiltrates were scored as followed:

(a) Tumoral Lymphoid Infiltrates:

The density of tumoral lymphoid infiltrates was quantified by countingthe small round lymphocytes distributed within the tumor epithelium andthe peritumoral stroma in five medium power fields (Nikon microscope,×20 objective). Immune-infiltrate density scored as 1 (weak), 2(moderate) or 3 (strong), was observed in 16%, 62% and 22% of theseries, respectively.

(b) Lymphoid Reaction at the Invasive Margin:

The cuff of lymphocytes abutting the deepest point of advancing tumor(invasive margin) was judged as, conspicuous (score 3), inconspicuous(score 2) or absent (score 1). The lymphoid reaction scored as 1, 2 or3, was observed in 18%, 60% and 22% of the series, respectively.

(c) Lymphoid Nodules Surrounding the Periphery of the Tumor:

Crohn's-like lymphoid reaction was defined as lymphoid aggregates (oftenwith germinal centers) ringing the periphery of invasive carcinomatypically found at the interface of the muscularis propria externa andpericolic fibro-adipose tissue, not associated with either mucosa (eg,diverticular origin) or pre-existing lymph node. Two large lymphoidaggregates in a section were required for the presence of this feature(score 2). More than two large lymphoid aggregates referred to score 3,whereas only one or an absence of lymphoid aggregates was scored 1. Thedensity of lymphoid nodules scored as 1, 2 or 3, was observed in 38%,39% and 23% of the series, respectively.

Real-Time Taqman PCR Analysis

Total RNA was extracted from 100 randomly selected frozen tumorspecimens from the cohort of 959 cases; 75 samples of sufficient qualityand quantity were analyzed for gene expression using quantitativereal-time TaqMan-PCR (Low-Density-Arrays) and the 7900 roboticPCR-system (Applied-Biosystems, Foster City, Calif.), as disclosedhereunder.

Tissue samples were snap-frozen within 15 minutes following surgery andstored in liquid N₂. Randomly selected frozen tumor specimens (n=100)from the cohort were extracted for RNA. Total RNA was isolated byhomogenization with RNeasy isolation-kit (Qiagen, Valencia, Calif.). Theintegrity and the quantity of the RNA were evaluated on abioanalyzer-2100 (Agilent Technologies, Palo Alto, Calif.). Seventy-fivesamples were of sufficient RNA quality and quantity forLow-Density-Array analysis. These samples, representative of the cohort,were all assessed for gene expression analysis. RT-PCR experiments werecarried-out according to the manufacturer's instructions(Applied-Biosystems, Foster City, Calif.). Quantitative real-timeTaqMan-PCR was performed using Low-Density-Arrays and the 7900 roboticreal-time PCR-system (Applied-Biosystems) (see list of genes in Table 2for details). 18S and GAPDH primers and probes were used as internalcontrols. Data were analyzed using the SDS Software v2.2(Applied-Biosystems).

Tissue samples were snap-frozen within 15 minutes following surgery andstored in liquid N₂. Randomly selected frozen tumor specimens (n=100)from the cohort (n=959), were extracted for RNA. Total RNA was isolatedby homogenization with RNeasy isolation-kit (Qiagen, Valencia, Calif.).The integrity and the quantity of the RNA were evaluated on abioanalyzer-2100 (Agilent Technologies, Palo Alto, Calif.). Seventy-fivesamples were of sufficient RNA quality and quantity forLow-Density-Array analysis. These samples, representative of the cohort,were all assessed for gene expression analysis. RT-PCR experiments werecarried-out according to the manufacturer's instructions(Applied-Biosystems, Foster City, Calif.). Quantitative real-timeTaqMan-PCR was performed using Low-Density-Arrays and the 7900 roboticreal-time PCR-system (Applied-Biosystems) (see list of genes in Table 2for details). 18S and GAPDH primers and probes were used as internalcontrols. Data were analyzed using the SDS Software v2.2(Applied-Biosystems).

Large-Scale Flow-Cytometric Analysis

Cells were extracted by mechanical dispersion from 39 fresh tumorsamples. All cells (including tumor cells) were analyzed byflow-cytometry. Cells from normal mucosa from a site that was distantfrom the fresh tumor were also analyzed. Cells were incubated for 30minutes at 4° C. with antibodies against immune cell markers (See Table3 for the list of antibodies). Analyses were performed with afour-color-FACScalibur flow cytometer and CellQuest software (BectonDickinson, San Diego, Calif.). Immune subpopulations were measured as apercentage of the total number of all cells and a percentage of thenumber of total CD3+ cells. Average-linkage hierarchical clustering wasapplied and the results were displayed using the GENESIS program (SturnA, Quackenbush J, Trajanoski Z. Genesis: cluster analysis of microarraydata. Bioinformatics 2002; 18(1):207-8; Galon J, Franchimont D, Hiroi N,et al. Gene profiling reveals unknown enhancing and suppressive actionsof glucocorticoids on immune cells. Faseb J 2002; 16(1):61-71; softwareavailable at http://www.genome.tugraz.at).

Tissue Microarray Construction

Using a tissue-array instrument (Beecher Instruments, ALPHELYS, Plaisir,France), two representative areas of the tumor (center and invasivemargin) were removed (0.6 mm and 1 mm-diameter punches, respectively)from paraffin-embedded tissue-blocks that were prepared at the time ofresection. Of the colonic carcinomas that were resected between 1990 and2003, 50 percent (415) were randomly selected for construction of tissuemicroarrays. Based on T, N, M, VELIPI pathologic findings, patients withthese tumors were representative of the entire cohort. Tissue-coresarrayed into recipient paraffin-blocks were cut into 5 μm sections forHarris's hematoxylin (HE) and immunohistochemical staining.

Immunohistochemistry

Following antigen retrieval and quenching of endogen-peroxidaseactivity, sections were incubated (60 min. at room temperature) withmonoclonal antibodies against CD45RO and CD3 (Neomarkers, Fremont,Calif.). Envision+ system (enzyme-conjugated polymer backbone coupled tosecondary antibodies) and DAB-chromogen were applied (Dako, Copenhagen,Denmark). Tissue sections were counterstained with Harris' hematoxylin.Isotype-matched mouse monoclonal antibodies were used as negativecontrols. Slides were analyzed using an image analysis workstation (SpotBrowser®, ALPHELYS). Polychromatic high-resolution spot-images (740×540pixel, 1.181 μm/pixel resolution) were obtained (×100 foldmagnification). Measurements were recorded as the number of positivecells per-tissue surface-unit.

Statistical Analysis

Kaplan-Meier curves were used to assess the influence of pathologicsigns of early metastatic invasion (VELIPI) on overall and disease-freesurvival. The significance of various clinical parameters was assessedby univariate analysis using the log-rank test (Table 1). We used a Coxproportional-hazards model to test the simultaneous influence onsurvival (overall and disease-free) of all covariates found significantin the univariate analysis. The same tests were used to assess theeffect of the density of CD45RO (number of cells/mm²) on overall anddisease-free survival, alone or together with the tumor, node, andmetastases (T, N, M) staging covariates. The Anova-t test and theWilcoxon-Mann-Whitney test were the parametric and non-parametric testsused to identify markers with a significantly different expression amongVELIPI-positive and VELIPI-negative tumors. Normality of the logarithmof the gene expression levels and of the CD45RO densities was determinedusing the Shapiro test. The Wilcoxon test was used to assess thesignificance of the difference between median survivals across differentgroups of patients. All tests were two-sided. A P-value<0.05 wasconsidered statistically significant. All P-values are reported withoutmultiple correction adjustments. All analyses were performed with thestatistical software R and Statview.

Material and Methods of Example 5

A.3. Patients and Database

The records of 415 colorectal cancer (CRC) patients who underwent aprimary resection of their tumor at the Laennec-HEGP Hospitals between1990 and 2003 were reviewed. These 415 patients from Laennec-HEGPHospitals were the main subject of our study (Table 11-S1).

Paraffin-embedded tumor samples were available from 150 consecutivepatients diagnosed with CRCs in Avicenne Hospital between 1996 and 2001.Finally, 119 patients of this validation series, not lost to follow-upand with biopsy samples from the 2 tumor regions (Tissue-Microarrayspots) available, were evaluated for the survival analyses. These 119patients (12, 33, 48, and 26 patients with UICC-TNM stages I, II, III,and IV, respectively) from Avicenne Hospital were the first validationset.

Frozen tumor samples from 75 patients from Laennec-HEGP Hospitals wereselected for gene expression analysis. These patients are different fromthe main series of 415 patients. From this series of 75 patients (6, 17,24, and 28 patients with UICC-TNM stages I, II, III, and IV,respectively), paraffin-embedded tumor samples from 69 patients, withbiopsy samples from the 2 tumor regions (Tissue-Microarray spots)available, were evaluated for the survival analyses. These 69 patientsfrom Laennec-HEGP Hospitals were the second validation set.

The observation time in these cohorts was the interval between diagnosisand last contact (death or last follow-up). Data were censored at thelast follow-up for patients without relapse, or death. The mean durationof follow-up of the main series was 45.3 months. The min:max valuesuntil progression/death or last follow-up were (0:166) months,respectively. Time to recurrence or disease-free time was defined as theinterval from the date of surgery to confirmed tumor relapse date forrelapsed patients and from the date of surgery to the date of lastfollow-up for disease-free patients. Histopathological and clinicalfindings were scored according to the UICC-TNM staging system (L. Sobin,C. Wittekind, TNM classification of malignant tumors. 6th, Ed.(Wiley-Liss, New York, 2002). Postsurgery patient surveillance wasperformed at Laennec-HEGP, Avicenne and associated Hospitals for allpatients according to general practice for CRC patients. Adjuvantchemotherapy was administered to patients with stage III CRCs, tohigh-risk stage II CRCs, and palliative chemotherapy to patients withadvanced colorectal cancers (stage IV) and to patients without completeresection of the tumor. Adjuvant chemotherapy was fluorouracil(FU)-based. Follow-up data were collected prospectively and updated. Asecure Web-based database, TME.db (Tumor MicroEnvironment Database,access available upon request), was built on a 3-tier architecture usingJava-2 Enterprise-Edition (J2EE) to integrate the clinical data and thedata from high-throughput technologies.

A.4. Histopathology

Real-Time Taqman PCR Analysis

Real time Taqman PCR analysis was performed such as described in theMaterial and Methods of Examples 1 to 4 above.

Tissue Microarray Construction

Tissue Microarray Construction was performed such as described in theMaterial and Methods of Examples 1 to 4 above.

Immunochemistry

Tissue microarray sections were incubated (60 min. at room temperature)with monoclonal antibodies against CD3 (SP7), CD8 (4B11), CD45RO (OPD4),GZMB (GrB-7), cytokeratin (AE1AE3) and cytokeratin-8 (Neomarkers,Fremont, Calif.). Envision+ system (enzyme-conjugated polymer backbonecoupled to secondary antibodies) and DAB-chromogen were applied (Dako,Copenhagen, Denmark). Double stainings were revealed withphosphate-conjugated secondary antibodies and FastBlue-chromogen. Forsingle stainings, tissue sections were counterstained with Harris'hematoxylin. Isotype-matched mouse monoclonal antibodies were used asnegative controls. Slides were analyzed using an image analysisworkstation (Spot Browser®, ALPHELYS). Polychromatic high-resolutionspot-images (740×540 pixel, 1.181 □m/pixel resolution) were obtained(×100 fold magnification). The density was recorded as the number ofpositive cells per unit tissue surface area. For each duplicate, themean density was used for statistical analysis.

For each tumor, the duplicate of spots showed a good level ofhomogeneity of stained cell densities in each tumor region (CT and IM).Heterogeneous densities of the immune infiltrates (HiLo and LoHi)between tumor regions (CT/IM) were present in 37%, 33%, 47%, 36% of thetumors assessed for CD3, CD8, CD45RO and GZMB cell densities,respectively.

Statistical Analysis

Genesis clustering software (J. Galon et al., 2002, Faseb J, Vol. 16:61)was used to visualize the correlation matrix presented in FIG. 1 and toperform Pearson un-centered hierarchical clustering. The pvcluster Rpackage was used to validate the found clusters.

Kaplan Meier estimators of survival were used to visualize the survivalcurves and to obtain the estimators of the median, 75^(th) percentileand survival rates at 2, 4 and 5 years for OS and DFS. The log-rank testwas used to compare disease-free and overall survival between patientsin different groups. For the markers measured with RT-PCR, the medianexpression level was taken as cut-off to dichotomize the variables. Forthe four markers further studied in two different regions (CT and IM)using TMA (CD3, CD45RO, CD8, GZM) the “minimum p-value” approach wasapplied to obtain the cutoff providing the best separation between thegroups of patients related to their disease-free survival outcome. Thecut-off values for CD3, CD8, CD45RO, GZMB cell densities were 370, 80,80, 30 cells/mm² in the center of the tumor and 640, 300, 190 and 60cells/mm² in the invasive margin, respectively. Because the P-valuesobtained in this way might present severe over-fitting, DFS log-rankP-values were corrected using the formula proposed by Altman et al (D.G. Altman et al., 1994, J. Natl Cancer Inst, Vol. 86:829). Additionally,DFS and OS log-rank P-values were calculated using 2-foldcross-validation after (D. Faraggi et al., 1996, Stat Med, Vol.15:2203). 100 repetitions were performed (with and without stratifyingon the grouping variable). The median p-values are summarized in Tables13-14 (S4-S5) and 16-17 (S7-S8).

A multivariate Cox proportional hazards model was applied to theCD3_(CT)CD3_(IM) combined regions marker to determine its hazard ratioafter adjustment by traditional histopathological tumor markers. The Coxmodel was applied only to patients with UICC-TNM, I, II, III toguarantee a common baseline hazard function. Hazard ratios from the DFSmodel were corrected using a shrinkage factor estimated fromleave-one-out cross-validation as suggested by Hollander et al (N.Hollander et al., 2004, Stat Med, Vol. 23:1701). Models using the medianas cutoff are presented in the Tables 14-15 and 18-19. Additionally, themarkers of interest were an independent prognostic factor whenconsidering the markers of interest in its original continuous scale(data not shown).

All through the text a P-value<0.05 was considered statisticallysignificant. All analyses were performed with the statistical software R(survival package) and Statview.

B. Results

Example 1: Correlation Between Clinical Outcome and Adaptive ImmuneResponse

1.1 Early Metastatic Invasion and Clinical Outcome

The prognostic significance of the presence of vascular emboli (VE),lymphatic invasion (LI), and perineural invasion (PI), which delineatedearly metastatic invasion (VELIPI), was investigated by univariateanalysis of the 959 colorectal cancer patients. VE, LI, PI, and VELIPIas well as the T, N, M stage significantly influenced disease-free andoverall survival (P<0.001) (Table 1).

The five-year disease-free survival rates were 32.4 percent amongpatients with VELIPI-negative tumors, and 12.1 percent among patientswith VELIPI-positive tumors, respectively. Differences were alsoobserved in the median duration of disease-free survival (3.3 vs 26.9months for VELIPI-positive and VELIPI-negative tumors, respectively,P<0.001). A similar pattern was found for overall survival (Table 1).

Furthermore, the presence of more than one sign of early metastaticinvasion conferred a worse prognosis that a single sign. Kaplan-Meiercurves suggested longer overall survival and disease-free survival inpatients with VELIPI-negative tumors than in patients withVELIPI-positive tumors (log-rank test, P<0.001). VE, LI, or PIcorrelated with the N and M stages (P<0.001 for all comparisons). Theinfluence of all significant covariates on survival was simultaneouslytested using a Cox proportional-hazards model. Multivariate analysisadjusting for TNM staging confirmed that VELIPI status was significantlyand independently associated with a better prognosis (P=0.04 and P=0.01for overall and disease free survival, respectively). Adjusting forDukes staging, VELIPI status was independently associated with a betterprognosis (P=0.007 and P=0.002 for overall and disease free survival,respectively).

1.2 Immune Cell Infiltration, Inflammation, Early-Metastatic Invasion,and Prognosis

Colorectal tumors (n=377) were assessed histopathologically for animmune cell infiltrate within the tumor and in the invasive margin.

The presence of strong immune infiltrate (score 3) was associated withVELIPI-negative tumors.

The mRNAs for proinflammatory and immunosuppressive molecules in 75colorectal tumors were measured by low density array quantitativereal-time PCR. No significant association between content of mRNA forproinflammatory mediators (IL-8, VEGF, CEACAM-1, MMP-7, Cox-2 andthrombospondin-1), or for immunosuppressive molecules (TGFβ, IL-10,B7-H3, and CD32b) and VELIPI status or relapse was found (FIG. 1 anddata not shown).

T-cells differentiate into T_(H)1 or T_(H)2 cells following expressionof T-bet or GATA-3, respectively. Protective immune responses aremediated by effector-memory T-cells with the phenotype CD8+, CD45RO+,CCR7−, CD62L−, perforin+, granulysin+, granzyme-B+. These cells exert animmediate effector function upon antigen stimulation by releasingcytotoxic mediators. As shown in FIG. 1, CD8a, granulysin, andgranzyme-B were increased in VELIPI-negative tumors and were furtherincreased in such tumors from patients who had not relapsed, as comparedwith VELIPI-positive tumors from patients who had relapsed (P<0.05).

Moreover, VELIPI-negative tumors from patients who had not relapsed hada significant increase in the T_(H)1 mediators T-bet, IRF-1, and IFN-γ□compared to VELIPI-positive tumors from patients who had relapsed(P<0.05). In contrast, the T_(H)2 transcription factor, GATA-3, was notincreased in either group of patients (FIG. 1).

Example 2: Phenotypes of Tumor-Infiltrating Immune Cells

Subpopulations of immune cells from 39 freshly resected colon cancerswere analyzed by large-scale flow-cytometry. To refine the analysis, 410different combinations of surface markers were measured by FACS, and theresults were plotted from the minimum to the maximum expression.

T-cells, B-cells, NK-cells, NKT-cells, and macrophages were analyzed inrelation to the VELIPI status of the tumors. CD3+ T-cells were the mostprevalent tumor-infiltrating immune cells. CD3+, CD3+CD4+, and CD3+CD8+T-cells were significantly increased (2.6, 2.5, 4.9 fold increase,respectively, P<0.05) in VELIPI-negative tumors compared toVELIPI-positive tumors.

Large-scale analysis of phenotypic and functional markers of T-cellsubpopulations (percentage of positive cells in the total populationisolated from the tumor and within the CD3+ T-cell population) revealeda significant difference (P<0.05) between VELIPI-negative andVELIPI-positive tumors for 65 different combinations of markers.Hierarchical clustering (Eisen M B, Spellman P T, Brown P O, Botstein D.Cluster analysis and display of genome-wide expression patterns. ProcNatl Acad Sci USA 1998; 95(25):14863-8) showed a homogeneous pattern inVELIPI-positive tumors, whereas two subgroups of VELIPI-negative tumorscould be distinguished.

All markers (CD45RO, CD45RA, CD27, CD28, CCR7, CD127) of the T-celldifferentiation process from naïve to effector-memory T-cells werepresent in the cluster of differentially expressed markers. Markers of Tcell migration (CD62L−, CCR7−, CD103, CD49d, CXCR3) and activation(HLA-DR, CD98, CD80, CD86, CD134) were also differentially expressedbetween VELIPI-negative and VELIPI-positive tumors.

The results have shown that naïve T cells (CD3+CCR7+) were rare in thetumors. By contrast, in the differentiation pathway from early-memory Tcells (CD45RO+CCR7-CD28+CD27+) to effector-memory T-cells(CD45RO+CCR7-CD28-CD27-), all subpopulations were detected. Comparedwith VELIPI-positive tumors, VELPI-negative tumors had significantlymore of these T cells (P<0.05). The results show that the highproportion of mature CD8+ T-cells in VELIPI-negative tumors. In contrastto tumors, distant normal mucosa from the same patients did not exhibitdifferences in the CD8+ T-cell subpopulations according to the VELIPIstatus.

Example 3: Effector-Memory T Cells and Survival

Immunohistochemical analysis on Tissue-MicroArrays prepared from 415colorectal cancers was performed. Staining with an anti-CD3 antibodyrevealed the presence of T-cells both within and at the invasive marginof the tumor. CD45RO+ cells were counted by automatic image software. Avalidation study showed a close correlation between optical andautomatic cell counts (R²=0.914, P<0.001).

VELIPI-negative tumors contained high numbers of CD45RO cells ascompared to VELIPI-positive tumors (P=0.02). In addition, high densityof memory T-cells was associated with lymph-node negative (N−) andmetastasis negative (M−) tumors (P<0.001).

Advanced stages of lymph-node invasion (N2, N3) were associated with lowdensities of CD45RO in tumors (FIG. 2).

Multivariate Cox proportional-hazard analysis showed that M (P<0.001), N(P=0.002), and T (P=0.004) as well as CD45RO (P=0.02) were independentprognostic factors for overall survival. Kaplan-Meier curves suggestedlonger overall survival and disease-free survival (FIG. 3A, 3B) inpatients with tumors containing high density of CD45RO than in patientswith low density (log-rank test, P<0.001). Patients whose tumorscontained high density of CD45RO had a median disease-free survival of36.5 months and a median overall survival of 53.2 months, as comparedwith 11.1 and 20.6 months, respectively, among patients with low densityof CD45RO (P<0.001 for all comparisons) (FIG. 3A, 3B). The five-yearoverall (FIG. 3A) and disease-free survival (FIG. 3B) rates were 46.3and 43.1 percent among patients with tumors containing high density ofCD45RO and 23.7 and 21.5 percent among patients with tumors containinglow density of CD45RO.

Results of Examples 1 to 3 above show that there exists a relationbetween pathologic signs of early metastatic invasion (vascular emboli(VE), lymphatic invasion (LI), and perineural invasion (PI),collectively termed VELIPI) and the outcome in 959 colorectal cancers.

It has also been shown the existence of an association between theVELIPI status of the tumor and evidence of an immune response within thetumor. In particular, an analysis of 39 colorectal cancers showed thatthe presence of intratumoral effector-memory T-cells, defined by CD3,CD8, CD45RO, CCR7, CD28, and CD27 markers, was associated withVELIPI-negative tumors. Analysis of 415 colorectal tumors showed thathigh density of infiltrating CD45RO+ cells correlated with a goodclinical outcome.

In the series of 959 colorectal cancers herein, emboli detected bymeticulous pathological examination showed a significant, independentassociation between VELIPI status and overall survival.

In examples 1 to 3 above, no significant differences were found in thecontent of mRNAs for proinflammatory and immunosuppressive molecules inVELIPI-positive and VELIPI-negative tumors, or in tumors from patientswho did or did not relapse. These findings suggest that inflammation isnot a factor in early metastatic invasion.

In contrast, there was increased mRNA for products and markers of T_(H)1effector T-cells (CD8, T-bet, IRF-1, IFN-γ, granulysin, and granzyme-B),and this increase was associated with prolonged survival and a lack ofpathological signs of early metastatic invasion.

Using tissue microarrays, the association between a high number ofCD45RO+ T-cells and the absence of lymphovascular and perineuralinvasion (P<0.002) was shown in Examples 1 to 3 above. Tumors thatcontained high density of effector-memory T cells were associated withlonger disease-free and overall survival than tumors lacking such cells(P<0.001). The presence of CD45RO-positive memory T-cells in the tumorwas an independent prognostic factor.

In examples 1 to 3, the high-throughput quantitative measurement ofcellular and molecular differences among colorectal cancers allowed adetailed characterization of the tumor micro-environment, andidentification of associations with clinical outcome. The experimentalresults show that the tumor microenvironment and the host's immuneresponse are of major importance in tumor progression.

Thus, it has been shown in Examples 1 to 3 above that univariateanalysis showed significant differences in disease-free and overallsurvival according to the presence or absence of histological signs ofearly metastatic invasion (P<0.001). By multivariate Cox analysis,pathologic stage (T, N, M) (P<0.001) and early metastatic invasion(P=0.04) were independently associated with survival. Tumors lackingsigns of early metastatic invasion had infiltrates of immune cells andincreased mRNA for products of T_(H)1 effector T-cells (CD8, T-bet,IRF-1, IFN-γ, granulysin, and granzyme-B).

In contrast, neither proinflammatory mediators nor immunosuppressivemolecules were differentially expressed. In tumors with or without earlysigns of metastatic invasion there were significant differences for 65combinations of T-cell markers, and hierarchical clustering showed thatmarkers of T cell migration, activation, and differentiation wereincreased in tumors lacking these signs.

These tumors contained increased numbers of CD8-positive T cells,ranging from early-memory (CD45RO+CCR7-CD28+CD27+) to effector-memory(CD45RO+CCR7-CD28-CD27-) T-cells. The presence of infiltrating memoryCD45RO+ cells, evaluated by immunohistochemistry, correlated with signsof early metastatic invasion, pathological stage, and survival.

It has thus been shown that signs of an immune response withincolorectal cancers are associated with the absence of pathologicalevidence of early metastatic invasion and prolonged survival.

Example 4: Correlation Between (i) Adaptive Immune Response and (i)Recurrence and Survival Times

Functional orientation of the host-response within colorectal cancerswas investigated by quantitative real-time PCR through the evaluation of18 immune-related genes. These genes were variably expressed among the75 tumours studied.

Correlation analyses performed between all genes (representing 153correlation tests) showed 70 significant combinations (P<0.05) including39 highly significant combinations (P<0.0001) (See Table 4).

A correlation-matrix was generated, followed by unsupervisedhierarchical clustering offering a convenient way to visualize patternsof similarity and difference among all correlations. This allowed theidentification of a dominant cluster of co-modulated genes, composed ofT_(H1) (T-bet, IRF-1, IFNγ) and immune-adaptive (CD3ζ, CD8, GLNY, GZMB)related genes, and two clusters referring to proinflammatory andimmune-suppressive mediators.

Expression patterns of the clusters were almost mutually exclusive inthe tumours. Expression levels of genes from the T_(H1)/adaptive clusterinversely correlated with relapse, whereas the others (VEGF, MMP-7,Cox-2, IL-8, Survivin, CEACAM1, TRAIL-R, B7H3, IL-10, TGFb) did not.

A hierarchical tree structure classifying the 75 colorectal cancersaccording to the mRNA levels of genes from the T_(H1)/adaptive cluster(from maximal to minimal expression levels) showed progressiverecurrence rates from 20% to 80%, (Fisher exact test comparing group 1and group 2, P=0.016). Patients with a homogeneous increased pattern ofT_(H1)/adaptive gene expression in tumour were associated with the bestprognosis.

Altogether, these data provided evidence for a beneficial effect of insitu T_(H1)/adaptive immunity on clinical outcome.

Then, the cellular end results of the immune-adaptive gene expressionprofiles was assessed by immunohistochemical-based Tissue-MicroArraysanalysis of 415 tumours.

Furthermore, distribution of the in situ adaptive immune response wasexplored by spotting the centre of the tumour (CT) along with theinvasive margin (IM).

In both tumour regions, immunostaining for total T lymphocytes (CD3),CD8 T cell effectors and associated cytotoxic molecule (GZMB), andmemory T cells (CD45RO) showed a wide spectrum of positive-immune celldensities among all sample tested.

The 6640 corresponding immunostainings were analysed with a dedicatedimage analysis workstation for signal quantification (captured spot),allowing precise cell density measurements.

A validation study showed a close correlation between optical andautomatic cell counts (R²>0.9, P<0.001 for all markers).

Immune cell distributions in specific regions were analysed in relationto the clinical outcome. Tumours from patients without relapse had asignificantly higher immune cell density (CD3, CD8, CD45RO, GZMB) withineach tumour region (CT or IM) (all P<0.003), than tumours from patientswith relapse (FIG. 4).

Based on computer-signal quantification, a mean was devised to test thecut-off values of stained cells densities (for all markers in bothtumour regions) for discrimination of patients for disease-free andoverall survival times (1600 Log-rank tests). This permitted to definethe optimal cut-off values of immune cell densities (CD3, CD8, CD45RO,GZMB), and to show that there was a large range of cut-off values in thetwo tumour regions that were significant (FIG. 5). According to thesecut-off values, it was observed that immune-cell infiltrates (high orlow densities for CD3, CD8, CD45RO, GZMB) in each region of the tumour(CT or IM) markedly distinguished patients (n=415) into groups withdifferent median disease-free survival (DFS) (FIG. 5). Log-rank testswere highly significant for all markers studied in both tumour regionsfor DFS (P-values ranging from 1.5×10⁻⁴ to 1.4×10⁻⁸) (FIG. 5 and Table5) and for OS (Table 6).

It was further investigated whether the architectural distribution ofthe immune cells populations within the tumour (CT/IM) could influenceprognosis. Kaplan-Meier curves for DFS and OS were analysed for patientswith high or low CD3 densities in both tumour regions. This showed thathigh CD3_(CT)/CD3_(IM) densities resulted in significantly betteroverall and disease-free survival as compared to high CD3 density in asingle region (P<0.0001) (FIG. 7A, 7B). The combined analysis of CT plusIM regions further increased median DFS differences between patientswith High and Low densities for all adaptive immune markers (P-valuesranging from 3.7×10⁻⁷ to 5.2×10⁻¹¹), as compared to single analysis ofCT or IM regions (FIG. 6 and FIG. 7C). Thus, median DFS for low- andhigh-patients were of 5.9 vs 45.9 months for CD3_(CT), 12.9 vs 47.8months for CD3_(IM) (FIG. 6), and of 5.9 vs 66.2 months forCD3_(CT)/CD3_(IM), respectively (FIG. 7C and Table 4). Taken together,these observations indicate that disease-free and overall survival timescan be predicted on the basis of the architectural distribution and ofthe amplitude of the in situ coordinated adaptive immune response indistinct tumour regions.

Colorectal cancer prognosis is currently based on histopathologiccriteria of tumour invasion. Cox proportional-hazards regression-modelsadjusted for TNM-stages and tumour differentiation showed thatCD3_(CT)/CD3_(IM) density was an independent prognosticator fordisease-free and overall survival (P=2.8×10⁻⁶, P=3.0×10⁻³, respectively)(Table 5). Remarkably, CD3_(CT)/CD3_(IM) densities was the mostsignificant parameter associated with disease-free survival and had abetter P-value than those of T and N stages for overall survivalanalysis. Furthermore, all adaptive immune markers also presented withan independent prognostic value adjusted for TNM-stages and tumourdifferentiation for disease-free and overall survival (Table 7).

Conventional staging of colorectal cancer does not account for themarked variability in outcome that exits within each stage. As thenature and the amplitude of the in situ immune response hold a strongindependent prognostic value, we investigated whether evaluation of thiscoordinated immune response could further predict patient outcomes ateach stage. We stratified patients according to Dukes' classification(FIG. 8a ) and showed an influence of CD3_(CT)/CD3_(IM) at all stages ofthe disease (FIG. 8b ).

Unexpectedly, we found that a strong in situ coordinated adaptive immuneresponse correlated with an equally favourable prognosis regardless ofthe tumour invasion through the intestinal wall and extension to thelocal lymph-nodes (Dukes classification A, B, C). Conversely, a weak insitu adaptive immune response correlated with a very poor prognosis evenin patients with minimal tumour invasion (Dukes classification A and B)(FIG. 8b ).

In examples 1 to 3 above, it was demonstrated that the absence of earlysigns of tumour dissemination (lymphovascular and perineural invasion)and of invasion of lymph-nodes was associated with the presence of astrong density of intratumoral effector-memory T-cells (T_(EM)).

It is in the present example further determined whether additionalevaluation of memory (CD45RO+) cell density to the CD3+ T cell densityin both tumour regions further discriminate patients at risk of tumourrecurrence. CD3_(CT)/CD3_(IM)/CD45RO_(CT)/CD45RO_(IM) markedlystratified patients in two groups with high and low risk of tumourrecurrence (FIG. 8c ). Strikingly, low densities of these markers inboth tumour regions revealed similar outcome for patients of Dukes C, B,and even A stages as compared to patients with concomitant distantmetastasis (Dukes D).

Here, using high-throughput quantitative measurement of cellular andmolecular immune parameters, the immunological forces in themicroenvironment of human colorectal carcinoma was characterised.

Whatever the role of immunosurveillance and shaping, the present dataclearly demonstrate the concept that once human colorectal carcinomasbecome clinically detectable, in situ natural anti-tumour immunity playsa major role in the control of tumour recurrence following surgicalexcision.

Beneficial in situ adaptive immune responses were not restricted topatients with minimal tumour invasion, indicating that in situimmunological forces might persist along with tumour progression. Thepossibility cannot be excluded that intra-tumoral lymphocytes modifytumour stroma or tumour cells, or both, in such a way that theyattenuate the metastatic capacity of tumour cells.

However, the correlation of the expression of in situ adaptive immunemarkers, T_(H1)-associated molecules, and cytotoxic mediators with a lowincidence of tumour recurrence provides evidence for immune-mediatedrejection of persistent tumour cells following surgery.

In this way, the good prognosis value associated with the presence insitu of high density of memory-T cells (CD45RO positive cells), probablyresults from the critical trafficking properties and the long-lastinganti-tumour protection capacity of these cells, as shown in micemodel¹⁸.

The present evaluation of the in situ adaptive immune response that isperformed, using quantitative measurements of immune cell densities atboth the centre and the margin of the tumour, uncovered the importanceof a coordinated adaptive immune response to control tumour recurrence.

Unexpectedly, the immunologic criteria, that are herein used, not onlyhad a prognostic value that was superior and independent of those of theTNM and Dukes classifications, but also correlated with an equallyfavourable prognosis regardless of the tumour invasion.

Time to recurrence and overall survival time is shown to be governedmore by the state of the local adaptive immune response rather than thepresence of tumour spreading through the intestinal wall and to regionallymph node(s).

This novel insight has several important implications and may change theunderstanding of the evolution of carcinoma, including colorectalcarcinoma. In addition, the criteria that have been used, should lead toa re-evaluation of the currently used classification of colorectalcarcinoma, and indicate with more precision, those patients at high-riskof tumour recurrence who may benefit from adjuvant therapy (includingimmunotherapy).

The utility of immunohistochemistry, combined with the availability ofan extensive set of antibodies against immune markers, should facilitatethe application of our approach to other tumours.

Example 5: Additional Results Relating to the Correlation Between (i)Adaptive Immune Response and (i) Recurrence and Survival Times

Genomic and in situ immunostaining analyses were conducted on 75 and 415patients, respectively (Table 11-S1). The data were entered into adedicated Tumoral MicroEnvironment Database (TME.db; access availableupon request). We used quantitative real-time PCR to evaluate theexpression levels of immune-genes related to inflammation, T-helper 1(T_(H1)) adaptive immunity, and immunosuppression. These genes showedvariable expression patterns in the 75 tumors studied. Correlationanalyses performed between all genes showed 39 highly significantcombinations (P<0.0001) (Table 4). A dominant cluster of co-modulatedgenes for T_(H1) adaptive immunity was identified (T-box transcriptionfactor 21 (T-bet), interferon regulatory factor 1 (IRF-1), IFNγ, CD3ζ,CD8, granulysin (GLNY), granzyme B (GZMB)). A hierarchical treestructure classifying the patients according to the expression levels ofgenes from this cluster revealed an inverse correlation betweenexpression of these genes and tumor recurrence (P value comparingpatient groups, all P<0.05). These data shows that T_(H1) adaptiveimmunity has a beneficial effect on clinical outcome.

Tissue-MicroArrays were next used to investigate the in situ adaptiveimmune response in the center of the tumor (CT) and the invasive margin(IM) of 415 CRCs. Immunostaining for total T lymphocytes (CD3), CD8 Tcell effectors and their associated cytotoxic molecule (GZMB), andmemory T cells (CD45RO) were quantified using a dedicated image analysisworkstation. Tumors from patients without recurrence had higher immunecell densities (CD3, CD8, GZMB, CD45RO) within each tumor region (CT,IM), than did those from patients whose tumors had recurred, as it wasformerly observed in Example 4 above. In each tumor region (CT, IM) andfor each marker (CD3, CD8, GZMB, CD45RO) there was a statisticallysignificant correlation between immune cells density and patientsoutcome for a large range of cut-off values (FIG. 9-S5). In particular,using the cut-off that yielded the minimum P-value for disease-freesurvival, the densities of CD3+, CD8+, GZMB+, and CD45RO+ cells in eachtumor region (CT and IM) allowed to stratify patients into groups withstatistically different disease-free survival (P-values corrected after(23), ranging from 1.0×10⁻² to 4.8×10⁻⁶) and overall survival (P-valuesranging from 5.5×10⁻³ to 7.9×10⁻⁸) (and Tables 12, 13-S3, S4).Re-analyses of the data using 100 repetitions of 2-foldcross-validations after (24) (Tables 12, 13-S3, S4) or setting thecut-off at the median of the data sets (Tables 14, 15-S5, S6), providedconcordant results as to the prognostic value of each immune parameter.

It was then investigated whether the combined analysis of tumor regionscould improve the prediction of patients' survival. For all the markersof adaptive immunity (CD3, CD8, GZMB and CD45RO), the combined analysisof CT plus IM regions (HiHi vs LoLo) increased disease-free and overallsurvival time differences between patients, as compared to single regionanalysis (Hi vs Lo) (Tables 12-15-S3-S6). Data were also analyzed using2-fold cross-validation after (24) (100*CV for each marker), showinghighly significant differences (Tables 12, 13-S3, S4). CD3_(CT)/CD3_(IM)was associated with the smallest P values for disease-free and overallsurvival analyses (P=7.6×10⁻⁸ and P=4.0×10⁻⁷ respectively) (Tables 12,13-S3, S4). To confirm these results, an additional cohort of patientsdifferent from the first series and a third cohort of CRCs from anotherhospital were analyzed. For each cohort, the median cut-off values forCD3_(CT)/CD3_(IM) (50% of patients with a high- and 50% of patients witha low-density) were determined. The two independent cohorts confirmedthe data obtained on the first series. All statistical analyses werealso performed in the subgroup of patients without concomitant distantmetastasis (UICC-TNM stages I, II, and Ill). Significant P-values wereobserved for CD3_(CT/IM), CD8_(CT/IM) and CD45RO_(CT/IM) fordisease-free survival and overall survival analyses (Tables16-19-S7-S10).

It was determined whether these immune criteria could discriminatepatient outcome at each step of cancer progression. Patients werestratified according to the UICC-TNM classification (25) (FIG. 10A). Astrong in situ immune reaction in both tumor regions correlated with afavorable prognosis regardless of the local extent of the tumor andinvasion to regional lymph-nodes (stages I, II, and Ill). Conversely, aweak in situ immune reaction in both tumor regions correlated with apoor prognosis even in patients with minimal tumor invasion (stage I)(FIG. 10B). We recently demonstrated the importance of the density ofCD45RO+ memory T cells in limiting tumor dissemination of CRCs (22). Wefound that patients with low densities of CD3+ cells and CD45RO+ memoryT cells in both tumor regions (CT and IM) had a very poor prognosis,similar to patients with concomitant distant metastasis (stage IV) (FIG.10C). In multivariate analysis, after adjusting for tumor invasion(T-stage), tumor differentiation and lymph-node invasion (N-stage),CD3_(CT)/CD3_(IM) density (HiHi, Heterogeneous, LoLo) remained anindependent prognostic factor with the highest hazard ratio and thesmallest P-value in disease-free survival analysis (HR of 2.391;P=1.4×10⁻⁶ corrected after (26)) (Table 20-S11). CD3_(CT)/CD3_(IM)density was the unique independent parameter associated with overallsurvival (HR of 1.89 P=1.2×10⁻⁵) (Table 21-S12). The histopathologicalparameters were no longer associated with disease-free and overallsurvival in patients with coordinated high or low densities of theimmune markers in both tumor regions (HiHi versus LoLo) (Tables 20 and21-S11 and S12).

Further, as shown in FIGS. 11 and 12, the in vitro prognosis methodaccording to the invention may be successfully performed for theprognosis of various types of cancers, as illustrated by the predictionof the outcome of colon and rectum cancers.

Still further, as illustrated in FIG. 13, the in vitro prognosis methodaccording to the invention has proved highly reliable for prognosis ofthe outcome of cancers in patients undergoing cancers at an early stage.

Yet further, as illustrated in FIG. 14, the in vitro prognosis methodaccording to the invention has been shown to allow an accurate prognosisof outcome of cancers, using, as the one or more biological markers, acombination or a set of six biological markers, namely PDCD1LG1, VEGF,TNFRSF6B, IRF1, IL8RA and SELL, that were quantified by a geneexpression analysis method, more precisely, Real-Time PCR analysis.

In summary, the results presented in Example 5 show that once human CRCsbecome clinically detectable, the adaptive immune response plays a rolein preventing tumor recurrence.

A positive correlation was found between the presence of markers forT_(H1) polarization, cytotoxic and memory T cells and a low incidence oftumor recurrence.

Thus, it was found herein that the type, the density, and the locationof immune cells in CRCs had a prognostic value that was superior andindependent of those of the UICC-TNM classification (L. Sobin et al.,2002, TNIM classification of malignant tumors. 6^(th), Ed., Wiley-Liss,New York) These results show that time to recurrence and overallsurvival time are be mostly governed by the state of the local adaptiveimmune response.

The new immunological tool provided by the present invention may lead torevision of the current indicators of clinical outcome and may helpidentify the high-risk patients who would benefit the most from adjuvanttherapy.

TABLE 1 Disease-Free and Overal Survival (n = 959 patients) Table 1.Disease-free and Overall Survival (n = 959 patients) Disease FreeSurvival Overall Survival N. of Rate at Median Rate at Median patients 5yr % months P value* 5 yr % months P value* T stage <0.001 s <0.001 spTis 39 48.7 55.7 48.7 55.7 pT1 54 42.6 52.2 44.4 63.8 pT2 166 40.4 43.644.2 49.1 pT3 502 23.7 16.5 26.7 25.8 pT4 208 16.8 1.6 17.8 16.8 N stage<0.001 s <0.001 s N0 568 35.4 34.6 36.8 43.1 N+ 384 16.1 4.3 16.7 16.9 Mstage <0.001 p <0.001 s M0 747 34.5 32.0 37.6 41.1 M+ 212 0.5 0.1 0.912.3 Dukes <0.001 s <0.001 s classification A 83 47.0 55.6 47.0 55.6 B438 37.2 39.2 41.1 46.8 C 227 24.7 19.5 27.3 28.1 D 212 0.5 0.1 1.0 12.1Sex 0.30 0.30 ns 0.47 ns Male 494 25.9 16.4 28.5 29.4 Female 465 28.219.3 30.5 27.3 Localization 0.20 0.20 ns 0.14 ns RC 243 23.9 14.5 24.719.7 TC 51 7.8 9.2 9.8 22.2 LC 84 28.6 15.3 31.0 27.2 SC 298 26.8 14.729.5 29.5 R 287 32.4 32.1 36.5 40.4 Differentiation 0.25 ns 0.09 ns Well737 30.7 21.7 33.6 33.2 Moderate 187 14.4 9.3 15.6 17.8 Poor 35 17.1 2.617.1 11.6 Mucinous Colloid 0.057 ns 0.270 ns No 766 28.2 19.5 30.9 30.9Yes 193 22.3 14.9 23.8 21.8 N. of lymph 0.11 ns 0.69 ns nodes analyzed<8 426 34.0 31.0 37.1 40.0 ≥8 533 21.4 12.9 23.5 23.2 VE <0.001 s <0.001s No 797 31.0 23.6 33.9 34.1 Yes 162 7.4 1.4 8.0 13.9 LI <0.001 s <0.001s No 803 29.5 21.6 32.1 32.0 Yes 158 14.1 0.5 16.0 16.1 PI <0.001 s<0.001 s No 860 29.3 20.7 32.0 32.0 Yes 99 7.1 0.1 8.1 16.2 VELIPI<0.001 s <0.001 s (VE or LI or PI) No 703 32.4 26.9 35.5 35.5 Yes 25712.1 3.3 13.2 16.8 VE or LI <0.001 s <0.001 s No 716 31.0 24.4 35.2 35.0Yes 243 13.6 3.7 12.8 16.3 VE and LI <0.001 s <0.001 s No 884 28.3 19.731.2 31.0 Yes 75 12.0 0.2 9.3 11.9 VE and LI and PI <0.001 s <0.001 s No911 28.0 19.5 30.7 30.5 Yes 48 8.3 0.1 8.3 9.5 RC: Right Colon, TC:Transverse Colon, LC: Left Colon, SC: Sigmold Colo,; R: Rectum VE:Vascular Emboli, LI: Lymphatic Invasion, OI: Perineural Invasion *Pvalue LogRank test

TABLE 2 List of genes Gene Name Acc. number Chr. Loc. IL10 interleukin10 NM_000572 1q31-q32 IL8 interleukin 8 NM_000584 4q13-q21 IFNGinterferon, gamma NM_000619 12q14 TGFB1 transforming growth factor, beta1 NM_000660 19q13.2 PTGS2 Prostaglandin-endoperoxide synthase 2 (Cox2)NM_000963 1q25.2 CEACAM1 carcinoembryonic antigen-related cell adhesionNM_001712 19q13.2 molecule 1 IRF1 interferon regulatory factor 1NM_002198 5q31.1 MMP7 matrix metalloproteinase 7 (matrilysin, uterine)NM_002423 11q21-q22 VEGF vascular endothelial growth factor NM_0033766p12 GZMB granzyme B NM_004131 14q11.2 TBX21 T-box 21 (T-bet) NM_01335117q21.2 B7H3 B7 homolog 3 NM_025240 15q23-q24 CD8A CD8 antigen, alphapolypeptide (p32) NM_001768 2p12 GNLY Granulysin NM_006433 2p12-q11BIRC5 baculoviral IAP repeat-containing 5 (survivin) NM_001168 17q25CD3Z CD3Z antigen, zeta polypeptide (TiT3 complex) NM_198053 1q22-q23NM_000734 TNFRSF10A tumor necrosis factor receptor superfamily,NM_003844 8p21 member 10a CD4 CD4 antigen (p55) NM_000616 12pter-p12

TABLE 3 List of antibodies Antibody Common name Clone IsotypeFluorochrom specie Manufacturer CCR5 CCR5 45531 IgG2b FITC mouse R&Dsystems CCR7 CCR7 3D12 IgG2a PE rat BD pharmingen CD103 Integrin alpha EBer-ATC8 IgG1 PE mouse BD pharmingen CD119 IFN-gamma-R1 BB1E2 IgG2a FITCmouse serotec CD120a TNFR1 H398 IgG2a PE mouse serotec CD120b TNFR2MR2-1 IgG1 PE mouse serotec CD122 IL-2R-beta MIK-beta1 IgG2a FITC mouseserotec CD127 IL-7R-alpha R34.34 IgG1 PE mouse beckman coulter CD134OX40L-R ACT35 IgG1 FITC mouse BD pharmingen CD14 CD14 M5E2 IgG2a APCmouse BD pharmingen CD152 CTLA-4 BNI3 IgG2a APC mouse BD pharmingenCD154 CD40L TRAP1 IgG1 FITC mouse BD pharmingen CD178 FasL NOK1 IgG1 —mouse BD pharmingen CD183 CXCR3 1C6/CXCR3 IgG1 APC mouse BD pharmingenCD184 CXCR4 12G5 IgG2a APC mouse BD pharmingen CD19 CD19 HIB19 IgG1 FITCmouse BD pharmingen CD1a CD1a HI149 IgG1 FITC mouse BD pharmingen CD210IL-10R-alpha 3F9 IgG2a PE rat BD pharmingen CD25 IL-2R-alpha M-A251 IgG1APC mouse BD pharmingen CD26 Dipeptidyl- M-A261 IgG1 PE mouse BDpharmingen peptidase IV CD27 CD27 M-T271 IgG1 PE mouse BD pharmingenCD28 CD28 CD28.2 IgG1 APC mouse BD pharmingen CD3 CD3ε UCHT1 IgG1 FITCmouse BD pharmingen CD3 CD3ε UCHT1 IgG1 CyCr mouse BD pharmingen CD3CD3ε S4.1 IgG2a PE-Cy5 mouse serotec CD32 FcγRII AT10 IgG1 FITC mouseserotec CD4 CD4 RPA-T4 IgG1 FITC mouse BD pharmingen CD4 CD4 RPA-T4 IgG1PE mouse BD pharmingen CD44 CD44 G44-26 IgG2b APC mouse BD pharmingenCD45 CD45 HI30 IgG1 CyCr mouse BD pharmingen CD45Ra CD45Ra HI100 IgG2bFITC mouse BD pharmingen CD45Ro CD45Ro UCHTL1 IgG2a APC mouse BDpharmingen CD47 CD47 B6H12 IgG1 PE mouse BD pharmingen CD49d VLA-4 9F10IgG1 PE mouse BD pharmingen CD5 CD5 UCHT2 IgG1 PE mouse BD pharmingenCD54 ICAM-1 HA58 IgG1 PE mouse BD pharmingen CD56 CD56 B159 IgG1 PEmouse BD pharmingen CD62L L-selectin Dreg56 IgG1 FITC mouse BDpharmingen CD69 CD69 FN50 IgG1 APC mouse BD pharmingen CD7 CD7 M-T701IgG1 FITC mouse BD pharmingen CD8 CD8 RPA-T8 IgG1 APC mouse BDpharmingen CD8 CD8 HIT8a IgG1 PE mouse BD pharmingen CD80 B7.1 L307.4IgG1 PE mouse BD pharmingen CD83 CD83 HB15e IgG1 PE mouse BD pharmingenCD86 B7.2 FUN-1 IgG1 FITC mouse BD pharmingen CD95 Fas DX2 IgG1 APCmouse BD pharmingen CD97 CD97 VIM3b IgG1 FITC mouse BD pharmingen CD98CD98 UM7F8 IgG1 FITC mouse BD pharmingen CXCR6 CXCR6 56811 IgG2b PEmouse R&D systems GITR GITR polyclonal IgG — goat R&D systems HLA-DRHLA-DR G46.6(L243) IgG2a FITC mouse BD pharmingen ICOS ICOS C394.4A IgGPE mouse clinisciences IFNγRII IFNγRII polyclonal IgG — goat R&D systemsIL-18Rα IL-18Rα 70625 IgG1 PE mouse R&D systems KIR-NKAT2 KIR-NKAT2 DX27IgG2a FITC mouse BD pharmingen PD1 PD1 J116 IgG1 PE mouse clinisciencesStreptavidin Streptavidin — — APC — BD pharmingen TCR αβ TCR αβT10B9.A1-31 IgM FITC mouse BD pharmingen TGFRII TGFRII 25508 IgG1 FITCmouse R&D systems

TABLE 4 Correlation analysis table 4: Correlation analysis GenesCorrelation 95% CI P value CD8A-TBX21 0.902 (0.848/0.938) <0.0001CD3Z-CD8A 0.797 (0.694/0.868) <0.0001 CD3Z-TBX21 0.784 (0.676/0.859)<0.0001 B7H3-TGFB1 0.760 (0.643/0.843) <0.0001 IFNG-TBX21 0.759(0.635/0.844) <0.0001 CD4-CD8A 0.738 (0.612/0.828) <0.0001 CD8A-IFNG0.728 (0.592/0.823) <0.0001 CD4-TBX21 0.727 (0.597/0.820) <0.0001CD3Z-CD4 0.719 (0.586/0.815) <0.0001 CD4-TGFB1 0.678 (0.531/0.786)<0.0001 CD8A-GNLY 0.671 (0.522/0.781) <0.0001 IFNG-IRF1 0.664(0.505/0.779) <0.0001 GNLY-IFNG 0.663 (0.505/0.779) <0.0001 IRF1-TBX210.656 (0.502/0.770) <0.0001 IL8-PTGS2 0.643 (0.485/0.761) <0.0001GNLY-TBX21 0.627 (0.464/0.749) <0.0001 CD3Z-IRF1 0.617 (0.451/0.742)<0.0001 CD8A-IRF1 0.617 (0.451/0.742) <0.0001 CD3Z-GNLY 0.613(0.446/0.739) <0.0001 CD3Z-IFNG 0.605 (0.428/0.737) <0.0001 GZMB-IFNG0.604 (0.422/0.739) <0.0001 GNLY-IRF1 0.597 (0.425/0.727) <0.0001IL10-TGFB1 0.596 (0.424/0.726) <0.0001 CD8A-IL10 0.586 (0.411/0.719)<0.0001 CD4-IL10 0.583 (0.408/0.717) <0.0001 CD8A-GZMB 0.574(0.392/0.713) <0.0001 GZMB-TBX21 0.548 (0.359/0.693) <0.0001 CD3Z-GZMB0.538 (0.347/0.687) <0.0001 CD4-IRF1 0.520 (0.330/0.670) <0.0001GNLY-GZMB 0.520 (0.324/0.673) <0.0001 B7H3-IL10 0.517 (0.326/0.668)<0.0001 CD4-GZMB 0.507 (0.309/0.663) <0.0001 GZMB-IRF1 0.504(0.305/0.661) <0.0001 IL10-TBX21 0.494 (0.297/0.650) <0.0001 CD4-IFNG0.493 (0.289/0.655) <0.0001 B7H3-CD4 0.475 (0.275/0.636) <0.0001CD8A-TGFB1 0.466 (0.264/0.628) <0.0001 CD3Z-IL10 0.459 (0.255/0.623)<0.0001 CD4-GNLY 0.454 (0.250/0.619) <0.0001 TBX21-TGFB1 0.433(0.226/0.603) 0.0001 GNLY-IL10 0.413 (0.202/0.587) 0.0002 CD3Z-TGFB10.398 (0.185/0.575) 0.0004 IFNG-IL10 0.390 (0.168/0.575) 0.0009B7H3-VEGF 0.371 (0.155/0.554) 0.0011 B7H3-IL8 0.370 (0.152/0.553) 0.0012CEACAM1-IRF1 0.359 (0.140/0.544) 0.0017 IL10-IRF1 0.355 (0.136/0.541)0.0019 IRF1-VEGF 0.351 (0.131/0.538) 0.0022 B7H3-MMP7 0.335(0.112/0.526) 0.0038 B7H3-PTGS2 0.333 (0.112/0.523) 0.0037 IRF1-TGFB10.333 (0.111/0.523) 0.0038 IL10-PTGS2 0.325 (0.103/0.517) 0.0047GZMB-IL10 0.320 (0.092/0.517) 0.0066 CD4-VEGF 0.316 (0.093/0.509) 0.0062GZMB-TGFB1 0.306 (0.076/0.504) 0.0097 IL8-MMP7 0.295 (0.068/0.493)0.0116 TBX21-VEGF 0.294 (0.069/0.491) 0.0113 CEACAM1-VEGF 0.292(0.066/0.489) 0.0119 TGFB1-VEGF 0.290 (0.065/0.488) 0.0124 BIRC5-IRF10.265 (0.037/0.466) 0.0234 GNLY-TGFB1 0.257 (0.029/0.460) 0.0278PTGS2-TGFB1 0.257 (0.028/0.459) 0.0281 MMP7-VEGF 0.251 (0.020/0.456)0.0332 IFNG-TGFB1 0.239 (0.001/0.452) 0.0492 IRF1-TNFRSF10A 0.238(0.009/0.444) 0.042 BIRC5-PTGS2 0.224 (−0.007/0.431)   0.0571 IL8-TGFB10.223 (−0.007/0.431)   0.0578 B7H3-IRF1 0.222 (−0.009/0.430)   0.059MMP7-TGFB1 0.221 (−0.012/0.430)   0.0622 B7H3-CD8A 0.216(−0.015/0.425)   0.0664 GZMB-VEGF 0.209 (−0.028/0.423)   0.0829CD3Z-VEGF 0.207 (−0.024/0.418)   0.0784 IFNG-IL8 0.206 (−0.034/0.424)  0.0922 CD3Z-CEACAM1 0.204 (−0.027/0.415)   0.0836 CD8A-VEGF 0.203(−0.028/0.414)   0.0846 IL10-IL8 0.196 (−0.036/0.408)   0.0967BIRC5-IFNG 0.195 (−0.045/0.414)   0.111 GZMB-IL8 0.194 (−0.043/0.410)  0.1087 B7H3-TBX21 0.191 (−0.041/0.403)   0.1056 B7H3-CD3Z 0.188(−0.044/0.401)   0.1109 CD4-MMP7 0.181 (−0.052/0.397)   0.1274CEACAM1-TBX21 0.174 (−0.059/0.388)   0.1416 GNLY-PTGS2 0.173(−0.059/0.388)   0.1435 MMP7-PTGS2 0.162 (−0.073/0.379)   0.1748BIRC5-GZMB 0.161 (−0.077/0.381)   0.1842 B7H3-GZMB 0.160(−0.078/0.381)   0.1862 CD4-TNFRSF10A 0.160 (−0.072/0.377)   0.176IFNG-TNFRSF10A 0.156 (−0.086/0.380)   0.2048 GNLY-TNFRSF10A 0.153(−0.079/0.370)   0.1957 TBX21-TNFRSF10A 0.147 (−0.086/0.365)   0.2157BIRC5-IL8 0.145 (−0.088/0.363)   0.2225 TNFRSF10A-VEGF 0.136(−0.097/0.355)   0.2518 B7H3-TNFRSF10A 0.135 (−0.098/0.355)   0.2541CD8A-TNFRSF10A 0.134 (−0.099/0.354)   0.2577 GZMB-PTGS2 0.134(−0.104/0.358)   0.2702 CEACAM1-TNFRSF10A 0.133 (−0.100/0.352)   0.2641B7H3-IFNG 0.126 (−0.116/0.353)   0.3088 IFNG-VEGF 0.123 (−0.119/0.351)  0.3177 CD3Z-TNFRSF10A 0.117 (−0.117/0.338)   0.3269 BIRC5-CEACAM1 0.109(−0.124/0.331)   0.3597 GNLY-IL8 0.106 (−0.128/0.328)   0.3754IFNG-PTGS2 0.106 (−0.136/0.336)   0.3903 GZMB-TNFRSF10A 0.104(−0.135/0.331)   0.3942 CEACAM1-IFNG 0.093 (−0.148/0.325)   0.4506B7H3-GNLY 0.090 (−0.143/0.313)   0.4514 BIRC5-GNLY 0.088(−0.145/0.311)   0.4628 CEACAM1-GZMB 0.087 (−0.151/0.316)   0.4736CEACAM1-GNLY 0.082 (−0.151/0.306)   0.4911 IL10-MMP7 0.081(−0.153/0.307)   0.499 IL8-VEGF 0.078 (−0.155/0.303)   0.5132 BIRC5-MMP70.077 (−0.157/0.304)   0.5192 CD8A-CEACAM1 0.076 (−0.157/0.301)   0.5232TGFB1-TNFRSF10A 0.071 (−0.162/0.296)   0.5538 BIRC5-VEGF 0.065(−0.168/0.291)   0.5855 IRF1-PTGS2 0.064 (−0.169/0.289)   0.594IRF1-MMP7 0.063 (−0.171/0.290)   0.6012 PTGS2-VEGF 0.063(−0.170/0.289)   0.5995 CEACAM1-MMP7 0.035 (−0.199/0.264)   0.7742IL10-TNFRSF10A 0.032 (−0.199/0.261)   0.786 IL8-IRF1 0.021(−0.211/0.249)   0.8633 CD4-CEACAM1 0.014 (−0.217/0.243)   0.9088BIRC5-TBX21 0.013 (−0.218/0.242)   0.9124 IFNG-MMP7 0.009(−0.231/0.249)   0.9402 CD3Z-MMP7 0.005 (−0.227/0.236)   0.968CEACAM1-PTGS2 −0.001 (−0.231/0.229)   0.9923 IL10-VEGF −0.004(−0.234/0.226)   0.9721 CD8A-PTGS2 −0.008 (−0.238/0.222)   0.9448GZMB-MMP7 −0.008 (−0.244/0.229)   0.947 IL8-TNFRSF10A −0.017(−0.246/0.214)   0.8892 GNLY-VEGF −0.023 (−0.252/0.208)   0.8484PTGS2-TBX21 −0.036 (−0.264/0.196)   0.7631 MMP7-TBX21 −0.049(−0.277/0.185)   0.6844 BIRC5-CD8A −0.051 (−0.278/0.181)   0.6675CD3Z-PTGS2 −0.051 (−0.278/0.181)   0.6683 BIRC5-CD3Z −0.054(−0.280/0.179)   0.6528 B7H3-CEACAM1 −0.063 (−0.289/0.169)   0.5972PTGS2-TNFRSF10A −0.066 (−0.292/0.166)   0.5782 CD8A-MMP7 −0.086(−0.311/0.149)   0.4739 B7H3-BIRC5 −0.095 (−0.318/0.138)   0.4236CD4-IL8 −0.101 (−0.323/0.133)   0.3987 CEACAM1-IL8 −0.101(−0.323/0.132)   0.3979 CD4-PTGS2 −0.111 (−0.333/0.122)   0.3494CEACAM1-IL10 −0.111 (−0.333/0.122)   0.3495 IL8-TBX21 −0.131(−0.350/0.102)   0.2714 BIRC5-IL10 −0.134 (−0.353/0.099)   0.2583CD8A-IL8 −0.163 (−0.378/0.070)   0.1701 MMP7-TNFRSF10A −0.217(−0.427/0.015)   0.0668 BIRC5-TGFB1 −0.218 (−0.426/0.013)   0.0643BIRC5-CD4 −0.231 (−0.438/−0.001) 0.0489 CEACAM1-TGFB1 −0.239(−0.445/−0.010) 0.0413 GNLY-MMP7 −0.241 (−0.448/−0.010) 0.0408BIRC5-TNFRSF10A −0.243 (−0.448/−0.014) 0.0378 CD3Z-IL8 −0.258(−0.461/−0.030) 0.0272

TABLE 5 Disease Free Survival (DFS) Disease Free Survival (DFS) N. ofRate at Rate at Rate at Median Table 5 patients 2 yrs 4 yrs 5 yrs monthsP value GZM-CT 1.66E−06 Hi 163 57.66 46.62 41.71 36.5 Lo 191 35.93 26.0421.87 12.4 GZM-IM 9.42E−07 Hi 175 56.00 44.57 38.28 36.6 Lo 129 38.7631.00 26.36 12.9 CD45RO-CT 1.43E−08 Hi 294 51.70 40.81 36.05 27.4 Lo 6720.58 11.76 8.82 2.4 CD45RO-IM 2.16E−06 Hi 190 56.31 46.84 42.10 42.0 Lo178 35.19 24.58 20.11 12.4 CD8-CT 3.68E−08 Hi 227 54.18 43.17 38.32 31.1Lo 132 27.81 20.30 16.54 5.9 CD8-IM 1.53E−04 Hi 129 60.93 50.78 45.3149.2 Lo 185 40.64 29.41 24.59 16.6 CD3-CT 3.90E−08 Hi 192 60.41 48.9543.75 45.9 Lo 165 28.91 19.87 16.26 5.9 CD3-IM 3.37E−08 Hi 178 59.7749.72 44.69 47.8 Lo 175 35.42 24.57 20.00 12.9 GZM-CT/IM 3.67E−07 HiHi95 62.74 51.96 46.07 51.4 LoLo 80 39.43 29.57 25.36 12.9 CD45RO-4.57E−10 CT/IM HiHi 151 60.26 52.31 47.68 51.6 LoLo 41 16.66 11.90 9.521.8 CD8-CT/IM 4.61E−08 HiHi 96 65.62 55.20 50.00 59.2 LoLo 93 30.8521.27 17.02 5.9 CD3-CT/IM 5.20E−11 HiHi 109 69.72 61.46 55.04 66.2 LoLo93 27.95 19.35 13.97 5.9 Table 5

TABLE 6 Overall Survival (OS) Overall Survival (OS) N. of Rate at Rateat Rate at Median TABLE 6 patients 2 yr % 4 yr % 5 yr % months P valueGZM-CT 8.18E−07 Hi 163 62.58 50.31 43.56 50.2 Lo 191 48.17 32.46 25.1321.2 GZM-IM 1.27E−02 Hi 175 61.14 48.57 39.43 45.3 Lo 129 55.04 37.2129.46 29.1 CD45RO-CT 3.14E−09 Hi 294 57.82 45.92 38.78 34.9 Lo 67 37.3116.42 11.94 16.4 CD45RO-IM 7.68E−04 Hi 190 62.63 50.53 44.21 49.2 Lo 17846.63 30.90 23.03 19.8 CD8-CT 2.66E−07 Hi 227 59.47 48.02 40.53 42.5 Lo132 43.94 25.76 19.70 18.7 CD8-IM 1.22E−03 Hi 129 65.63 53.91 46.88 54.8Lo 185 54.84 37.10 28.50 29.6 CD3-CT 7.86E−08 Hi 192 65.63 55.21 47.4057.8 Lo 165 42.42 24.24 18.18 18.6 CD3-IM 9.08E−05 Hi 178 64.04 52.2546.07 52.6 Lo 175 48.57 32.57 24.00 21.4 GZM-CT/IM 1.50E−03 HiHi 9568.42 55.79 47.37 58.3 LoLo 80 58.75 37.50 28.75 32.0 CD45RO- 4.12E−07CT/IM HiHi 151 64.24 54.97 49.67 59.6 LoLo 41 39.02 14.63 12.20 17.7CD8-CT/IM 1.21E−06 HiHi 96 69.79 59.38 52.08 61.2 LoLo 93 49.46 29.0321.51 22.3 CD3-CT/IM 5.07E−08 HiHi 109 72.48 64.22 55.96 63.9 LoLo 9345.16 25.81 16.13 19.3 Table 6

TABLE 7 Multivariate proportional hazard analysis for DFS and OS Table7: Multivariate proportional hazard analysis for DFS Variable* Hazardratio 95% CI P T-stage 1.780 (1.348-2.362) 5.2E−05 N-stage 2.130(1.481-3.060) 4.5E−05 Differentiation 1.110 (0.777-1.584) 5.7E−01CD3_(CT)/CD3_(IM) patterns 0.570 (0.450-0.721) 2.8E−06 Variable* Hazardratio 95% CI P T-stage 1.700 (1.275-2.268) 3.1E−04 N-stage 2.117(1.449-3.093) 1.1E−04 Differentiation 0.969 (0.676-1.389) 8.6E−01CD8_(CT)/CD8_(IM) patterns 0.614 (0.480-0.786) 1.1E−04 Variable* Hazardratio 95% CI P T-stage 1.880 (1.441-2.452) 3.3E−06 N-stage 2.298(1.599-3.301) 6.8E−06 Differentiation 1.035 (0.736-1.457) 8.4E−01CD45RO_(CT)/ 0.564 (0.439-0.723) 6.2E−06 CD45RO_(IM) patterns Variable*Hazard ratio 95% CI P T-stage 1.777 (1.334-2.37) 8.5E−05 N-stage 2.449(1.651-3.63) 8.3E−06 Differentiation 1.049 (0.707-1.56) 8.1E−01GZMB_(CT)/GZMB_(IM) patterns 0.591 (0.459-0.76) 4.3E−05 Table 7:Multivariate proportional hazard analysis for OS Variable Hazard ratio95% CI P T-stage 1.335 (1.052-1.693) 1.7E−02 N-stage 1.657 (2.989-6.595)3.6E−03 M-stage 4.440 (1.179-2.328) 1.5E−13 Differentiation 1.058(0.748-1.496) 7.5E−01 CD3_(CT)/CD3_(IM) patterns 0.726 (0.587-0.897)3.0E−03 Variable Hazard ratio 95% CI P T-stage 1.376 (1.070-1.769)1.3E−02 N-stage 1.575 (1.100-2.254) 1.3E−02 M-stage 4.467 (2.966-6.729)8.0E−13 Differentiation 0.966 (0.679-1.375) 8.5E−01 CD8_(CT)/CD8_(IM)patterns 0.712 (0.571-0.888) 2.5E−03 Variable Hazard ratio 95% CI PT-stage 1.396 (1.114-1.750) 3.7E−03 N-stage 1.684 (1.204-2.355) 2.3E−03M-stage 4.160 (2.805-6.170) 1.4E−12 Differentiation 0.935 (0.677-1.292)6.9E−01 CD45RO_(CT)/ 0.703 (0.558-0.885) 2.8E−03 CD45RO_(IM) patternsVariable Hazard ratio 95% CI P T-stage 1.360 (1.071-1.73) 1.2E−02N-stage 1.710 (1.188-2.46) 3.9E−03 M-stage 4.392 (2.866-6.73) 1.1E−11Differentiation 1.094 (0.752-1.59) 6.4E−01 GZMB_(CT)/GZMB_(IM) patterns0.905 (0.722-1.14) 3.9E−01 *M stratified

TABLE 8 Gene Percentage Nr, No Nr, LogRank LOLO vs HiHi vs CombinationsStatus1 Status2 Relapse Relapse Relapse p-value Combination CombinationIRF1 GNLY 1 1 25 27 9 0.00000637 1.26E−04 1 IRF1 GNLY 0 0 71.42857143 1025 0.00000637 1 1.26E−04 PDCD1LG1 GNLY 1 1 32.43243243 25 12 0.000019573.89E−04 1 PDCD1LG1 GNLY 0 0 75 9 27 0.00001957 1 3.89E−04 PDCD1LG2 IRF11 1 25.71428571 26 9 0.00003780 2.71E−04 1 PDCD1LG2 IRF1 0 0 70.5882352910 24 0.00003780 1 2.71E−04 PDCD1LG1 IRF1 1 1 30.23255814 30 130.00003942 4.85E−04 1 PDCD1LG1 IRF1 0 0 69.04761905 13 29 0.00003942 14.85E−04 PDCD1LG1 GNLY 0 1 40 9 6 0.00005604 0.02553386 0.74929474PDCD1LG1 GNLY 1 0 40 9 6 0.00005604 0.02553386 0.74929474 IRF1 IL8 1 126.66666667 22 8 0.00008324 6.56E−04 1 IRF1 IL8 0 0 72.4137931 8 210.00008324 1 6.56E−04 ICOS GNLY 0 1 33.33333333 12 6 0.000096690.00648539 1 ICOS GNLY 1 0 47.36842105 10 9 0.00009669 0.0695250.55885623 ICOS GNLY 1 1 35.29411765 22 12 0.00009903 0.00148605 1 ICOSGNLY 0 0 75 8 24 0.00009903 1 0.00148605 TNFRSF6B IRF1 1 1 27.2727272724 9 0.00010448 4.71E−04 1 TNFRSF6B IRF1 0 0 71.875 9 23 0.00010448 14.71E−04 PDCD1LG2 GNLY 1 1 28.57142857 25 10 0.00010559 7.00E−04 1PDCD1LG2 GNLY 0 0 70.58823529 10 24 0.00010559 1 7.00E−04 IRTA2 GNLY 1 126.92307692 19 7 0.00010819 7.02E−04 1 IRTA2 GNLY 0 0 76 6 19 0.000108191 7.02E−04 IRF1 GNLY 1 0 50 8 8 0.00013118 0.20696761 0.11061678 IRF1GNLY 0 1 56.25 7 9 0.00013118 0.34514115 0.05584765 STAT1 PDCD1LG1 1 131.03448276 20 9 0.00015364 0.00411737 1 STAT1 PDCD1LG1 0 0 70 9 210.00015364 1 0.00411737 STAT1 IRF1 1 1 25 21 7 0.00017203 0.00136119 1STAT1 IRF1 0 0 68.96551724 9 20 0.00017203 1 0.00136119 GATA3 CD8A 0 169.23076923 4 9 0.00018062 0.3355095 0.02232563 GATA3 CD8A 1 084.61538462 2 11 0.00018062 0.04841363 0.00103488 GNLY CXCL9 1 131.42857143 24 11 0.00018108 0.00167576 1 GNLY CXCL9 0 0 70.58823529 1024 0.00018108 1 0.00167576 TBX21 GNLY 1 1 30.3030303 23 10 0.000211964.58E−04 1 TBX21 GNLY 0 0 75 8 24 0.00021196 1 4.58E−04 TNFRSF6BPDCD1LG1 1 1 31.25 22 10 0.00021333 9.67E−04 1 TNFRSF6B PDCD1LG1 0 074.19354839 8 23 0.00021333 1 9.67E−04 IRF1 ICOS 1 1 27.77777778 26 100.00021448 0.00164508 1 IRF1 ICOS 0 0 67.64705882 11 23 0.00021448 10.00164508 IL8 CD4 1 0 33.33333333 18 9 0.00021968 3.65E−04 0.39718035IL8 CD4 0 1 37.93103448 18 11 0.00021968 5.91E−04 0.58270625 TBX21 IRF11 1 25 27 9 0.00022445 3.28E−04 1 TBX21 IRF1 0 0 68.57142857 11 240.00022445 1 3.28E−04 PDCD1LG2 IRF1 0 1 47.05882353 9 8 0.000227930.13051259 0.20662362 PDCD1LG2 IRF1 1 0 58.82352941 7 10 0.000227930.53057151 0.03154075 GNLY CD4 1 0 22.22222222 14 4 0.00023630 2.62E−040.22728471 GNLY CD4 0 1 45 11 9 0.00023630 0.03419617 1 MMP7 IRF1 1 1 2521 7 0.00025303 4.06E−04 1 MMP7 IRF1 0 0 74.07407407 7 20 0.00025303 14.06E−04 TNF PDCD1LG1 0 1 29.41176471 12 5 0.00027386 0.002051340.75798695 TNF PDCD1LG1 1 0 41.17647059 10 7 0.00027386 0.02760881 1TNFRSF6B GNLY 1 1 30.3030303 23 10 0.00027678 0.00119011 1 TNFRSF6B GNLY0 0 71.875 9 23 0.00027678 1 0.00119011 TGFB1 IRF1 1 1 29.03225806 22 90.00028329 8.07E−04 1 TGFB1 IRF1 0 0 73.33333333 8 22 0.00028329 18.07E−04 PDCD1LG2 PDCD1LG1 1 1 31.70731707 28 13 0.00031033 0.00180905 1PDCD1LG2 PDCD1LG1 0 0 67.5 13 27 0.00031033 1 0.00180905 TGFB1 IRF1 0 138.0952381 13 8 0.00031619 0.02015558 0.5556315 TGFB1 IRF1 1 057.14285714 9 12 0.00031619 0.24654453 0.05090421 TBX21 PDCD1LG1 1 127.77777778 26 10 0.00033814 8.35E−04 1 TBX21 PDCD1LG1 0 0 68.5714285711 24 0.00033814 1 8.35E−04 TNF GNLY 1 1 34.48275862 19 10 0.000339974.10E−04 1 TNF GNLY 0 0 82.14285714 5 23 0.00033997 1 4.10E−04 PDCD1LG1IRF1 0 1 44.44444444 5 4 0.00035285 0.24937129 0.45141556 PDCD1LG1 IRF11 0 55.55555556 4 5 0.00035285 0.45891075 0.24655356 IRF1 CXCL9 1 1 3028 12 0.00036339 0.00157043 1 IRF1 CXCL9 0 0 66.66666667 13 260.00036339 1 0.00157043 TBX21 GNLY 0 1 42.10526316 11 8 0.000376390.03467136 0.54586516 TBX21 GNLY 1 0 47.36842105 10 9 0.000376390.069525 0.24596116 PTGS2 IRF1 1 1 30.76923077 18 8 0.000381600.00500586 1 PTGS2 IRF1 0 0 72 7 18 0.00038160 1 0.00500586 IRF1 ART1 10 20 8 2 0.00038775 0.01498501 0.6043956 IRF1 ART1 0 1 66.66666667 3 60.00038775 0.4965035 0.31468531 INDO GNLY 1 1 35.29411765 22 120.00039262 0.00707271 1 INDO GNLY 0 0 69.6969697 10 23 0.00039262 10.00707271 PDCD1LG1 ICOS 1 1 31.57894737 26 12 0.00039662 0.00489333 1PDCD1LG1 ICOS 0 0 66.66666667 12 24 0.00039662 1 0.00489333 PDCD1LG2GNLY 0 1 47.05882353 9 8 0.00039685 0.13051259 0.22435124 PDCD1LG2 GNLY1 0 52.94117647 8 9 0.00039685 0.23279471 0.12613911 TNFRSF6B TBX21 1 124 19 6 0.00041496 5.44E−04 1 TNFRSF6B TBX21 0 0 75 6 18 0.00041496 15.44E−04 PDCD1 IRF1 1 1 25.64102564 29 10 0.00043410 5.64E−04 1 PDCD1IRF1 0 0 65.78947368 13 25 0.00043410 1 5.64E−04 IRF1 IFNG 1 127.02702703 27 10 0.00043964 7.09E−04 1 IRF1 IFNG 0 0 68.75 10 220.00043964 1 7.09E−04 TNF GNLY 0 1 34.7826087 15 8 0.00044402 0.001226081 TNF GNLY 1 0 43.47826087 13 10 0.00044402 0.00736128 0.57364958 IL8GNLY 1 1 31.03448276 20 9 0.00046499 0.00136119 1 IL8 GNLY 0 0 75 7 210.00046499 1 0.00136119 TNFRSF6B IFNG 1 1 25 21 7 0.00046651 9.10E−04 1TNFRSF6B IFNG 0 0 72 7 18 0.00046651 1 9.10E−04 PDCD1LG1 PDCD1 1 127.02702703 27 10 0.00047832 9.77E−04 1 PDCD1LG1 PDCD1 0 0 66.6666666712 24 0.00047832 1 9.77E−04 IRTA2 GNLY 0 1 42.30769231 15 11 0.000484860.0227009 0.3822739 IRTA2 GNLY 1 0 56 11 14 0.00048486 0.232095470.04831826 PDCD1LG1 CXCL9 1 1 34.14634146 27 14 0.00050191 0.0038056 1PDCD1LG1 CXCL9 0 0 67.5 13 27 0.00050191 1 0.0038056 IL8 ICOS 1 139.28571429 17 11 0.00050590 0.00668911 1 IL8 ICOS 0 0 76.92307692 6 200.00050590 1 0.00668911 TNF IRF1 1 1 31.42857143 24 11 0.000540726.84E−04 1 TNF IRF1 0 0 73.52941176 9 25 0.00054072 1 6.84E−04 IL8 ICOS0 1 40 15 10 0.00055140 0.01074613 1 IL8 ICOS 1 0 41.66666667 14 100.00055140 0.01996838 1 IRF1 IL8 1 0 40.90909091 13 9 0.000575670.04324338 0.37231571 IRF1 IL8 0 1 59.09090909 9 13 0.000575670.37716737 0.02446738 PDCD1 GNLY 1 1 31.42857143 24 11 0.000580246.84E−04 1 PDCD1 GNLY 0 0 73.52941176 9 25 0.00058024 1 6.84E−04TNFRSF6B CXCL9 1 1 32.14285714 19 9 0.00063900 0.00694187 1 TNFRSF6BCXCL9 0 0 71.42857143 8 20 0.00063900 1 0.00694187 GNLY CXCL10 1 134.28571429 23 12 0.00064897 0.00374119 1 GNLY CXCL10 0 0 70.58823529 1024 0.00064897 1 0.00374119 SELL GNLY 1 1 32.14285714 19 9 0.000652550.00105233 1 SELL GNLY 0 0 77.77777778 6 21 0.00065255 1 0.00105233 SELLIRF1 1 1 26.66666667 22 8 0.00065555 6.56E−04 1 SELL IRF1 0 0 72.41379318 21 0.00065555 1 6.56E−04 MMP7 GNLY 1 1 31.03448276 20 9 0.000678780.00136119 1 MMP7 GNLY 0 0 75 7 21 0.00067878 1 0.00136119 PDCD1LG1EBAG9 1 1 37.93103448 18 11 0.00067896 0.03430795 1 PDCD1LG1 EBAG9 0 068.96551724 9 20 0.00067896 1 0.03430795 TGFB1 GNLY 1 1 29.03225806 22 90.00071504 0.00204606 1 TGFB1 GNLY 0 0 70 9 21 0.00071504 1 0.00204606IL18R1 GNLY 1 1 36.36363636 21 12 0.00075372 0.01304077 1 IL18R1 GNLY 00 68.75 10 22 0.00075372 1 0.01304077 IFNG GNLY 1 1 28.57142857 25 100.00075584 0.00119534 1 IFNG GNLY 0 0 70 9 21 0.00075584 1 0.00119534TNFRSF6B PDCD1LG2 1 1 26.66666667 22 8 0.00076477 0.00169422 1 TNFRSF6BPDCD1LG2 0 0 68.96551724 9 20 0.00076477 1 0.00169422 TNFRSF6B PDCD1 1 129.62962963 19 8 0.00081540 8.73E−04 1 TNFRSF6B PDCD1 0 0 76.92307692 620 0.00081540 1 8.73E−04 INDO GNLY 0 1 33.33333333 12 6 0.000815850.01830705 1 INDO GNLY 1 0 55.55555556 8 10 0.00081585 0.367159260.23851585 TBX21 IL8 1 0 39.28571429 17 11 0.00081677 0.003723770.77529553 TBX21 IL8 0 1 46.42857143 15 13 0.00081677 0.009953960.40283859 TNFRSF8 IRF1 1 1 25.80645161 23 8 0.00082357 0.00458134 1TNFRSF8 IRF1 0 0 63.33333333 11 19 0.00082357 1 0.00458134 TNF PDCD1LG11 1 37.14285714 22 13 0.00083836 0.00148511 1 TNF PDCD1LG1 0 076.47058824 8 26 0.00083836 1 0.00148511 IRF1 EBAG9 1 1 35.71428571 1810 0.00085969 0.01512429 1 IRF1 EBAG9 0 0 71.42857143 8 20 0.00085969 10.01512429 PDCD1LG1 IL18R1 1 1 37.14285714 22 13 0.00087042 0.01603268 1PDCD1LG1 IL18R1 0 0 67.64705882 11 23 0.00087042 1 0.01603268 IRTA2 IRF11 1 33.33333333 22 11 0.00087051 0.00113427 1 IRTA2 IRF1 0 0 75 8 240.00087051 1 0.00113427 IRF1 IL18R1 1 1 33.33333333 22 11 0.000872330.0063032 1 IRF1 IL18R1 0 0 68.75 10 22 0.00087233 1 0.0063032 LAT IL8 01 34.48275862 19 10 0.00091759 0.00148805 0.3996356 LAT IL8 1 041.37931034 17 12 0.00091759 0.00461551 0.77997357 SELL GNLY 0 1 37.5 159 0.00095410 0.00482852 0.77381721 SELL GNLY 1 0 50 12 12 0.000954100.04632871 0.25940735

TABLE 9 gene name alternate gene gene Public (old) name pathwaydescription RefSeq 18s endogenous endogenous X03205 control control ACEACE Tumor DCP, ACE1, NM_000789 DCP1, CD143, MGC26566 ACTB Beta Actinendogenous endogenous NM_003234 control control AGTR1 angiotensinangiogenesis AT1, AG2S, NM_031850.1 II receptor, AT1B, NM_000685.3 type1 AT2R1, HAT1R, AGTR1A, AGTR1B, AT2R1A, AT2R1B AGTR2 angiotensinangiogenesis AT2 NM_000686 II receptor, type 2 APC NM_000038 APOA1apolipoprotein MHC present sur NM_000039 A-I pathway cell tum et modulecytotox, interaction TCRg9d2 ARF1 p53 tumor suppressor NM_001658signalling AXIN1 Axin Axin NM_181050| NM_003502 BAX BAX Apoptosispathway NM_138763, NM_138765, NM_004324, NM_138761 BCL2 BCL2 Apoptosispathway NM_000633 BCL2L1 BCL-XL Apoptosis pathway NM_001191 CXCR5chemokine pathway NM_001716 BMP2 BMP2 TGF pathway Bone NM_001200morphogenetic protein 8a BRCA1 NM_007294| NM_007295| NM_007296|NM_007297| NM_007298| NM_007299| NM_007300| NM_007301| NM_007302|NM_007303| NM_007304| NM_007305| NM_007306 BTLA BTLA Adaptive B and TNM_181780 immunity lymphocyte associated C3 C3 C3 NM_000064 CASP3apoptosis apoptosis NM_004346 CASP9 caspase 9, apoptosis-relatedcysteine NM_001229.2 peptidase, Gene hCG25367 Celera Annotation CCL1CCL1 chemokine Chemokine NM_002981 pathway (C-C motif) ligand 1 CCL11Eotaxin chemokine Chemokine NM_002986 pathway (C-C motif) ligand 11CCL13 MCP-4 chemokine Chemokine NM_005408 pathway (C-C motif) ligand 13CCL16 HCC-4 chemokine CC NM_004590 pathway Chemokines & Receptors CCL17TARC chemokine Chemokine NM_002987 pathway (C-C motif) ligand 17 CCL18PARC chemokine CC NM_002988 pathway Chemokines & Receptors CCL19 Mip-3c,MIP- chemokine SCYA19 NM_006274 3 beta pathway CCL2 MCP-1 chemokineSCYA2 NM_002982 pathway CCL20 MIP-3 alpha chemokine Chemokine NM_004591pathway (C-C motif) ligand 20 CCL21 6Ckine chemokine chemokine NM_002989pathway (C-C motif) ligand 21 CCL22 CCR4 ligand, chemokine chemokineNM_002990 MDC, T-reg pathway (C-C motif) spec ligand 22 CCL23 MPIF-1chemokine chemokine NM_145898| pathway (C-C motif) NM_005064 ligand 23CCL24 Eotaxin-2 chemokine chemokine NM_002991 pathway (C-C motif) ligand24 CCL25 TECK chemokine chemokine NM_148888| pathway (C-C motif)NM_005624 ligand 25 CCL26 Eotaxin-3 chemokine chemokine NM_006072pathway (C-C motif) ligand 26 CCL27 CTACK chemokine chemokine NM_006664pathway (C-C motif) ligand 27 CCL28 CCL28 chemokine chemokine NM_019846|pathway (C-C motif) NM_148672 ligand 28 CCL3 Mip-1a chemokine SCYA3NM_002983 pathway CCL5 Rantes chemokine SCYA5 NM_002985 pathway CCL7MCP-3 chemokine chemokine NM_006273 pathway (C-C motif) ligand 7 CCL8MCP-2 chemokine chemokine NM_005623 pathway (C-C motif) ligand 8 CCNB1cell cycle NM_031966 CCND1 cell cycle NM_053056 CCNE1 cell cycleNM_001238| NM_057182 CCR1 chemokine CC NM_001295 pathway Chemokines &Receptors CCR10 CCR10 chemokine chemokine NM_016602 pathway (C-C motif)receptor 10 CCR2 CCR2 chemokine pathway NM_000647 CCR3 CCR3 chemokinechemokine NM_178329| pathway (C-C motif) NM_001837 receptor 3 CCR4 CCR4chemokine chemokine NM_005508 pathway (C-C motif) receptor 4 CCR5 CCR5chemokine pathway NM_000579 CCR6 CCR6 chemokine chemokine NM_031409|pathway (C-C motif) NM_004367 receptor 6 CCR7 CCR7 chemokine pathwayNM_001838 CCR8 CCR8 chemokine chemokine NM_005201 pathway (C-C motif)receptor 8 CCR9 CCR9 chemokine chemokine NM_006641 pathway (C-C motif)receptor 9 CCRL2 HCR chemokine CC Chemokines & pathway Receptors CD154CD154 (TNFSF5) Adaptive immunity NM_000074 CD19 B cells Adaptiveimmunity NM_001770 CD1a CD1A Adaptive CD1A NM_001763 immunity antigen, apolypeptide CD2 CD2 antigen Adaptive immunity NM_001767 (p50), sheep redblood cell receptor CD226 PTA1; DNAM1; adhesion, activation NM_006566DNAM-1; TLiSA1 Summary: CD226 is a ~65 kDa glycoprotein expressed on thesurface of NK cells, platelets, monocytes and a subset of T cells. It isa member of the Ig-superfamily containing 2 Ig-like domains of the V-set and is encoded by a gene on human chromosome 18q22.3. CD226 mediatescellular adhesion to other cells bearing an unidentified ligand andcross-linking CD226 with antibodies causes cellular activation CD244CD244 natural killer cell receptor 2B4 NM_016382 PDCD1LG1 B7H1 Adaptiveimmunity NM_014143 CD28 CD28 Adaptive immunity NM_006139 CD34 CD34 CD34NM_001773 CD36 CD36 antigen (collagen type I receptor, NM_001001547|thrombospondin receptor) NM_001001548| NM_000072 CD38 CD38 Adaptiveimmunity NM_001775 CD3E CD3E Adaptive immunity NM_000733 antigen,epsilon polypeptide (TiT3 complex) CD3G CD3G Adaptive immunity NM_000073antigen, gamma polypeptide (TiT3 complex) CD3Z CD3Z Adaptive immunityNM_000734 antigen, zeta polypeptide (TiT3 complex) CD4 CD4 antigenAdaptive immunity NM_000616 (p55) CD40LG TNFSF5 Adaptive CD40L NM_000074immunity CD5 Adaptive immunity NM_014207 CD54 ICAM-1 Adaptive immunityNM_000201 CD6 Adaptive immunity NM_006725 CD68 CD68 Innate immunityNM_001251 CD69 Adaptive immunity NM_001781 CLIP CD74 MHC pathwayNM_001025158| antigen NM_001025159| (invariant NM_004355 polypeptide ofmajor histocompatibility complex, class II antigen- associated) CD80CD80 Adaptive immunity NM_005191 CD83 Adaptive immunity NM_004233 SLAMF5CD84 Adaptive immunity NM_003874 CD86 CD86 Adaptive immunity NM_006889CD8A CD8 Adaptive immunity NM_001768.1 CDH1 cadherin 1, adhesion, EmbolsNM_004360 type 1, E- metastasis cadherin (epithelial) CDH7 Adhesionadhesion NM_004361 CDK2 cell cycle NM_052827| NM_001798 CDK4 cell cycleNM_000075 CDKN1A CIP1 p21 mutation cyclin-dependent kinase and inhibitor1A (p21, Cip1) methylation DKN1B KIP1 p27 mutation cyclin- NM_004064 anddependent methylation kinase inhibitor 1B (p27, Kip1) CDKN2A p16INK4amutation cyclin- NM_000077; and dependent NM_058195; methylation kinaseNM_058197; inhibitor 2A (melanoma, p16, inhibits CDK4) CDKN2B CDKN2Bcyclin- NM_004936 dependent kinase inhibitor 2B (p15, inhibits CDK4)CEACAM1 (CD66a) Tumor NM_001024912| NM_001712 COL4A5 Collagen CollagenIV (COL4A5) NM_033381.1, IV (COL4A5) NM_033380.1, NM_000495.3 CREBBP CKpathway Histone NM_004380 acetyltransferase CRLF2 TSLP R Adaptiveimmunity NM_022148|NM_001012288 CSF1 colony stimulating factor 1 CSF-1NM_000757 (macrophage) CSF2 CSF-2 CSF-2 NM_000758 CSF3 CSF-3 CSF-3NM_000759 CTLA4 CD152 Adaptive CD152 NM_005214 immunity CTNNB1beta-catenin wnt pathway catenin NM_001904 wnt pathway (cadherin-catenin associated (cadherin- protein), associated beta 1, protein), 88kDa beta 1, 88 kDa CTSC Adaptive DNA NM_148170.2| immunity microarray TNM_001814.2 CX3CL1 Fractalkine chemokine chemokine NM_002996 pathway(C-X3-C motif) ligand 1 CX3CR1 CX3CR1 chemokine chemokine NM_001337pathway (C-X3-C motif) receptor 1 CXCL1 GRO alpha chemokine chemokineNM_001511 pathway (C—X—C motif) ligand 1 (melanoma growth stimulatingactivity, alpha) CXCL10 IP10 chemokine SCYB11 NM_001565 pathway CXCL11ITAC chemokine SCYB11 NM_005409 pathway CXCL12 SDF-1 chemokine chemokineNM_199168| pathway (C—X—C NM_000609 motif) ligand 12 (stromalcell-derived factor 1) CXCL13 BLC chemokine chemokine NM_006419 pathway(C—X—C motif) ligand 13 (B-cell chemoattractant) CXCL14 BRAK chemokineCXC NM_004887 pathway Chemokines & Receptors CXCL16 CXCR6 chemokinechemokine NM_022059 ligand, Th1 pathway (C—X—C motif) ligand 16 CXCL2GRO beta chemokine chemokine NM_002089 pathway (C—X—C motif) ligand 2CXCL3 GRO gamma chemokine chemokine NM_002090 pathway (C—X—C motif)ligand 3 CXCL5 ENA chemokine Chemokine NM_002994 pathway (C—X—C motif)ligand 5 CXCL6 GCP-2 chemokine chemokine NM_002993 pathway (C—X—C motif)ligand 6 (granulocyte chemotactic protein 2) CXCL9 MIG chemokine CXCNM_002416 pathway Chemokines & Receptors CXCR3 CXCR3 chemokine GPR9NM_001504 pathway CXCR4 CXCR4 chemokine chemokine NM_003467 pathway(C—X—C motif) receptor 4 CXCR6 CXCR6 chemokine chemokine NM_006564pathway (C—X—C motif) receptor 6 CYP1A2 CYP1A2 CYP1A2 NM_000761 CYP7A1CYP7A1 CYP7A1 NM_000780 DCC deleted in metastasis NM_005215.1 colorectalcarcinoma, Gene hCG1811785 Celera Annotation DCN DCN TGF pathway DecorinInh NM_133503| TGF NM_133504| NM_133505| NM_001920 DEFA6 DefensinDefense over NM_001926 alpha 6, over expression in expression in colon Kcolon K DICER1 Dicer1, Dcr- MRNA pathway NM_030621; 1 homolog NM_177438(Drosophila) DKK1 wnt pathway dickkopf NM_012242 homolog 1 (Xenopuslaevis) Dok-1 p62Dok TCR pathway exprimé parcell NM_001381hematopoietiques, role inh prolif T Dok-2 P56Dok-2, TCR pathway expriméNM_201349| FRIP dans les T, NM_003974 role Inh prolif T DOK6 NM_152721DVL1 DHS1 DHS1 homolog NM_181870| homolog (dishevelled) NM_182779|(dishevelled) NM_004421 E2F4 E2F4 TGF pathway E2F NM_001950transcription factor 4, p107/p130- binding EBI3 NM_005755 ECE1 ECE-1ECE-1 ECE NM_001397 ECGF1 metastasis metastasis NM_001953 EDN1endothelin 1, EDN1, hCG37405 NM_001955.2 Gene hCG37405 Celera AnnotationEGF epidermal NM_001963 growth factor (beta- urogastrone) EGFR predictedepidermal NM_005228 STRING growth factor receptor (erythroblasticleukemia viral (v-erb- b) oncogene homolog, avian) EIF4E eukaryotictranslation initiation factor 4E CD105 angiogenese angiogenesisNM_000118 Endoglin ENPEP glutamyl ENPEP, hCG21423 NM_001977.2aminopeptidase (aminopeptidase A), Gene hCG21423 Celera Annotation ERBB2NM_001005862| NM_004448 EREG epiregulin, Gene EREG, hCG14966 NM_001432.1hCG14966 Celera Annotation FCGR3A, CD16 CD16 NM_000569, FCGR3B NM_000570FN1 Fibronectin (FN Fibronectin (FN FN, CIG, NM_002026.1, precursor)precursor) FINC, LETS NM_054034.1 FOXP3 FOXP3 Adaptive forkhead boxNM_014009 immunity P3 FYN FYN TCR pathway FYN NM_153047| oncogeneNM_153048| related to NM_002037 SRC, FGR, YES FZD1 frizzled frizzledfrizzled NM_003505 homolog 1 (Drosophila) GAPD GAPDH endogenousendogenous NM_002046 control control GLI2 hedgehog GLI-KruppelNM_030379| pathway GLI- family NM_030380| Kruppel family memberNM_030381| member GLI2 GLI2 NM_005270 GNLY Granulysin (GNLY) Cytotox519, LAG2, NM_012483.1, pathway NKG5, LAG- NM_006433.2 2, D2S69E, TLA519GOLPH4 golgi Golgi required for NM_014498 phosphoprotein 4 transporterprotein U55853 transport from the er to the golgi complex GRB2 TCRNM_203506| pathway NM_002086 GSK3B wnt glycogen NM_002093 pathwaysynthase kinase 3 beta GSTP1 anti-oxidant anti- NM_000852 oxidant GUSBGUSB endogenous endogenous NM_000181 control control GZMA Adaptive DNANM_006144.2 immunity microarray T GZMB Granzyme Cytotox CCPI, CSPB,NM_004131 B (GZMB) pathway CGL-1, CSP- B, CTLA1 GZMH Adaptive DNANM_033423.2 immunity microarray T GZMK granzyme K (granzyme 3; DNANM_002104.1 tryptase II), Gene hCG40447 microarray T Celera AnnotationHLA-B HLA-B MHC major NM_005514 pathway histocompatibility complex,class I, B HLA-C HLA-C MHC major NM_002117 pathway histocompatibilitycomplex, class I, C HLA-DMA HLA-DMA MHC major NM_006120 pathwayhistocompatibility complex, class II, DM alpha HLA-DMB HLA-DMB MHC majorNM_002118 pathway histocompatibility complex, class II, DM beta HLA-DOAHLA-DOA MHC major NM_002119 pathway histocompatibility complex, classII, DO alpha HLA-DOB HLA-DOB MHC major NM_002120 pathwayhistocompatibility complex, class II, DO beta HLA-DPA1 HLA-DPA1 MHCmajor NM_033554 pathway histocompatibility complex, class II, DP alpha 1HLA-DQA2 HLA-DQA2 MHC major NM_020056 pathway histocompatibilitycomplex, class II, DQ alpha 2 HLA-DRA HLA-DRA MHC HLA-DRA NM_019111pathway HLX1 H2.0-like Th1 Th1 NM_021958.2 homeo box 1 (Drosophila),Gene hCG25119 Celera Annotation HMOX1 HO-1 HO-1 NM_002133 HRASexpression mutation v-Ha-ras NM_176795 of high levels and Harvey rat ofV12 methylation sarcoma HRAS - a viral constitutively oncogene active(and homolog therefore oncogenic) mutant of HRAS - leads to a prematuregrowth arrest that is similar in many respects to the arrest that isobserved when human cells reach replicative senescence 68. HSPB3 heatshock 27 kDa protein 3 NM_006308 HUWE1 UREB1, MHC ubiquitin, NM_031407Proteolysis; pathway arf, Protein Proteolysis; Protein metabolismmetabolism and and modification modification ICAM1 metastasis metastasisNM_000201 ICAM-2 CD102 Adaptive immunity NM_000873 ICOS ICOS Adaptiveimmunity NM_012092 ID1 inhibitor of DNA ID1, metastasis NM_002165.2binding 1, hCG37143 signature dominant negative helix- loop-helixprotein, Gene hCG37143 Celera Annotation ifna1 Ifna1 InterferonInterferon, NM_024013 pathway alpha 1 ifna17 Ifna17 InterferonInterferon, NM_021268 pathway alpha 17 ifna2 Ifna2 Interferoninterferon, NM_000605 pathway alpha 2 ifna5 Ifna5 Interferon interferon,NM_002169 pathway alpha 5 ifna6 Ifna6 Interferon interferon, NM_021002pathway alpha 6 ifna8 Ifna8 Interferon interferon, NM_002170 pathwayalpha 8 IFNAR1 ifnar1 Interferon Interferon NM_000629 pathway (alpha,beta and omega) receptor 1 IFNAR2 ifnar2 Interferon interferonNM_207584| pathway (alpha, beta NM_207585| and omega) NM_000874 receptor2 IFNG IFN-g Interferon IFG, IFI NM_000619 pathway IFNGR1 IFN-gamma R1Interferon pathway NM_000416 IFNGR2 IFN-gamma R2 Interferon pathwayNM_005534 IGF1 insulin-like growth pathway NM_000618 growth factor 1(somatomedin C) IHH hedgehog Indian NM_002181 pathway hedgehog Indianhomolog hedgehog (Drosophila) homolog (Drosophila) IKBKB IkB2 CK pathwayAF080158 IL10 IL-10 CK pathway NM_000572 IL12A IL-12p35 CK pathwayinterleukin NM_000882 12 IL12B IL-12p40 CK pathway interleukin NM_00218712 IL12RB1 IL-12 R beta 1 Adaptive interleukin NM_153701| immunity 12receptor, NM_005535 beta 1 IL12RB2 IL12RB2 CK pathway interleukinNM_001559 12 receptor, beta 2 IL13 IL-13 CK pathway NM_002188 IL13RA2interleukin IL13RA2, hCG20596 metastasis NM_000640.2 13 receptor,signature alpha 2, Gene hCG20596 Celera Annotation IL15 IL-15 CK pathwayNM_000585 IL15RA IL-15 R CK pathway interleukin NM_002189 alpha 15receptor, alpha IL17 IL-17 CK pathway interleukin NM_002190 17(cytotoxic T- lymphocyte- associated serine esterase 8) IL17R IL17R CKpathway interleukin NM_014339 17 receptor IL17RB IL17RB CK pathwayinterleukin NM_018725|NM_172234 17 receptor B IL18 IL-18 CK pathwayNM_001562 IL1A IL-1a CK pathway NM_000575 IL1B IL-1b CK pathwayNM_000576 IL1R1 IL-1 RI Adaptive immunity NM_000877 IL2 IL-2 CK pathwayNM_000586 IL21 IL-21 CK pathway Common NM_021803 gamma Chain ReceptorFamily IL21R IL-21 R CK pathway Common NM_181078| gamma Chain NM_181079|Receptor NM_021798 Family IL23A IL-23 CK pathway interleukin 23,NM_016584 alpha subunit p19 IL23R IL-23R CK pathway interleukin 23NM_144701 receptor IL24 IL24 CK pathway interleukin 24 NM_006850 IL27IL27 CK pathway interleukin 27 NM_145659 IL2RA CD25 CK pathway NM_000417IL2RB IL-2 R beta, CK pathway Adaptive NM_000878 CD122 immunity IL2RGIL-2 R CK pathway Adaptive NM_000206 gamma, immunity CD132 IL3 IL-3 CKpathway NM_000588 IL31RA IL31RA CK pathway interleukin 31 NM_139017receptor A IL4 IL-4 CK pathway NM_000589 IL4RA IL-4 R CK pathway Th1/Th2Cells NM_001008699| NM_000418 IL5 IL-5 CK pathway NM_000879 IL6 IL-6 CKpathway NM_000600 IL7 IL-7 CK pathway NM_000880 IL7RA IL-7 R alpha CKpathway Adaptive NM_002185 immunity IL8 CXCL8 inflammation CK pathwayNM_000584 pathway CXCR1 IL-8 RA Adaptive immunity NM_000634 CXCR2 IL-8RB chemokine CXC NM_001557 pathway Chemokines & Receptors IL9 IL-9 CKpathway NM_000590 IL9R IL-9 R CK pathway Common NM_176786 gamma ChainReceptor Family IRF1 interferon Adaptive immunity NM_002198 regulatoryfactor 1 ISGF3G interferon- Interferon interferon- NM_006084; stimulatedpathway stimulated transcription transcription factor 3, factor 3, gammagamma 48 kDa 48 kDa ITGA4 integrin adhesion, integrin, NM_000885 a4b7metastasis alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 receptor)ITGA7 integrin, adhesion, metastasis NM_002206 alpha 7 integrin, CD103Adaptive immunity NM_002208.3 alpha E (antigen CD103, human mucosallymphocyte antigen 1; alpha polypeptide), Gene hCG33203 CeleraAnnotation ITGB3 integrin, beta 3 metastasis NM_000212.2 (plateletglycoprotein IIIa, antigen CD61), Gene hCG27604 Celera Annotation JAK2Janus kinase 2 JAK2, hCG31308 NM_004972.2 (a protein tyrosine kinase),Gene hCG31308 Celera Annotation JAK3 cytokine pathway NM_000215 KLRB1Adaptive DNA NM_002258.2 immunity microarray T KLRC4 killer cell lectin-AF030313 NM_007360.1 like receptor subfamily C, member 4, killer celllectin-like receptor subfamily K, member 1, Gene hCG2009644 CeleraAnnotation KLRF1 Adaptive DNA NM_016523.1 immunity microarray T KLRG1killer cell lectin- AF097367 NM_005810.3 like receptor subfamily G,member 1, Gene hCG25214 Celera Annotation KRAS NM_004985 LAG3 immuneAdaptive lymphocyte- NM_002286 immunity activation gene 3 LAIR2 Adaptiveimmunity NM_002288| NM_021270 LEF1 lymphoid lymphoid enhancer- NM_016269enhancer- binding factor 1 binding factor 1 LGALS9 lectin,galactoside-binding, soluble, 9 NM_009587.1|NM_002308.2 (galectin 9),Gene hCG1749919 Celera Annotation LILRB3 leukocyte IRT5 Wangimmunosuppression immunoglobulin- microarray like receptor, subfamily B(with TM and ITIM domains), member 3 LRP2 MEGALIN MEGALIN NM_004525 LTATNF-b CK pathway LT, TNFB, NM_000595 TNFSF1 SLAMF3 CD229 Adaptiveimmunity NM_002348 MADCAM1 mucosal MADCAM1, hCG20569 NM_130760.1|vascular NM_130761.1 addressin cell adhesion molecule 1, Gene hCG20569Celera Annotation MADH3 MADH-3 TGF pathway SMAD3, NM_005902 JV15-2 MADH7MADH-7 TGF pathway MADH8, NM_005904 SMAD7 MAF c-maf Th2 CK pathway v-mafNM_005360 musculoaponeurotic fibrosarcoma oncogene homolog (avian)MAP2K1 NM_002755 MDM2 Mdm2, p53 pathway NM_002392; transformedNM_006878; 3T3 cell NM_006879; double NM_006880; minute 2, NM_006881;p53 binding NM_006882; protein (mouse) MICA MHC class I MHC present surNM_000247 polypeptide- pathway cell tum et related module sequence Acytotox MICB MHC class I MHC present sur NM_005931 polypeptide- pathwaycell tum et related module sequence B cytotox MKI67 proliferationproliferation NM_002417 MMP12 matrix adhesion, metastasis NM_002426metallopeptidase L23808 12 (macrophage elastase) MMP9 matrix adhesion,metastasis NM_004994 metallopeptidase 9 (gelatinase B, 92 kDagelatinase, 92 kDa type IV collagenase) MTA1 Metastasis metastasisNM_004689.2 associated 1, Gene hCG19442 Celera Annotation MTSS1Metastasis metastasis NM_014751.2 suppressor 1, Gene hCG2009512 CeleraAnnotation MYC MYC TGF v-myc NM_002467 pathway myelocytomatosis viraloncogene homolog (avian) MYD88 Toll-like pathway NM_002468 MYH6 MYH6MYH6 D00943 (Gene card) NCAM1 Adaptive immunity NFATC1 activation Tactivation T NM_172387| NM_172388| NM_172389| NM_172390| NM_006162 NKG7Adaptive DNA microarray T NM_005601.3 immunity NLK wnt nemo like kinaseNM_016231 pathway NOS2A iNos (Nos2A) iNos (Nos2A) NM_000625 P2X7Purinergic P2RX7, hCG1641456 NM_177427.2| receptor P2X, NM_002562.4ligand-gated ion channel, 7, Gene hCG1641456 Celera Annotation PDCD1PD-1 Adaptive immunity NM_005018 PECAM-1 CD31 Adaptive immunityNM_000442 CXCL4 PF4 chemokine CXC Chemokines NM_002619 pathway &Receptors PGK1 PGK1 endogenous endogenous NM_000291 control controlPIAS1 CK pathway protein NM_016166 inhibitor of activated STAT, 1 PIAS2CK pathway protein NM_173206| inhibitor of NM_004671 activated STAT, 2PIAS3 CK pathway protein NM_006099 inhibitor of activated STAT, 3 PIAS4CK pathway protein NM_015897 inhibitor of activated STAT, 4 PLATplasminogen PLAT, hCG17154 NM_033011.1| activator, NM_000930.2| tissue,Gene NM_000931.2 hCG17154 Celera Annotation PML tumor suppressor,interferon signalling NM_033238| NM_033239| NM_033240| NM_033242|NM_033244| NM_033245| NM_033246| NM_033247| NM_033249| NM_033250|NM_002675 PP1A PP1 PP1 NM_021130 CXCL7 NAP-2 chemokine pathway NM_002704PPP2CA PP2A NM_002715 PRF1 Perforin (PRF Cytotox P1, PFP, NM_005041 1)pathway HPLH2 PROM1 CD133, prominin 1, CD133 NM_006017 pentaspantransmembrane positive glycoprotein, fraction of human bone marrow, cordblood and peripheral blood have been shown to efficiently engraft inxenotransplantation models, and have been shown to contain the majorityof the granulocyte/macrophage precursors, PSMB5 proteasome MHC pathwayNM_002797 (prosome, macropain) subunit, beta type, 5 PTCH hedgehogpathway patched NM_000264 homolog (Drosophila) PTGS2 COX-2 inflammationpathway NM_000963 PTP4A3 protein metastasis NM_032611.1 tyrosinephosphatase type IVA, member 3 PTPN6 CK pathway protein NM_080548|tyrosine NM_080549| phosphatase, NM_002831 non-receptor type 6 PTPRCCD45 CD45 NM_002838, NM_080922, NM_080923, NM_080921 RAB23 hedgehogpathway RAB23, NM_183227| member RAS oncogene family NM_016277 RAC/RHONM_018890 RAC2 RAC2 RAC2 ras- NM_002872 related C3 botulinum toxinsubstrate 2 (rho family, small GTP binding protein Rac2) RAF NM_002880RB1 retinoblastoma 1 mutation and cell cycle NM_000321 (includingmethylation osteosarcoma) RBL1 retinoblastoma-like retinoblastoma-like 1NM_183404| 1 (p107) (p107) NM_002895 REN RENIN (REN) RENIN (REN)NM_000537 Drosha RNASEN, Droshan microRNA AJ242976 microRNA SELE CD62E(SELE) metastasis NM_000450 SELL CD62L NM_000655 SELP CD62E (SELP)metastasis NM_003005 SERPINE1 serpin peptidase SERPINE1, hCG17353NM_000602.1 inhibitor, clade E (nexin, plasminogen activator inhibitortype 1), member 1, Gene hCG17353 Celera Annotation SFRP1 secretedsecreted frizzled NM_003012 frizzled SIRP beta 1 Adaptive immunityNM_006065 SKI c-ski (SKI) c-ski (SKI) SKV NM_003036 SLAMF1 Adaptiveimmunity NM_003037 SLAMF6 NTB-A Adaptive immunity NM_052931 SLAMF7 CRACCAdaptive immunity NM_021181 SLAMF8 BLAME Adaptive immunity NM_020125SMAD2 SMAD2 TGF pathway SMAD, NM_001003652| mothers NM_005901 againstDPP homolog 2 (Drosophila) SMAD4 SMAD4 TGF pathway SMAD, NM_005359mothers against DPP homolog 4 (Drosophila) SMO, SMOH SMO SMO smoothenedNM_005631 homolog (Drosophila) SMURF1 SMURF1 SMURF1 SMADNM_181349|NM_020429 specific E3 ubiquitin protein ligase 1 SOCS1 CKpathway suppressor NM_003745 of cytokine signaling 1 SOCS2 CK pathwaysuppressor NM_003877 of cytokine signaling 2 SOCS3 CK pathway suppressorNM_003955 of cytokine signaling 3 SOCS4 CK pathway suppressor NM_199421|of cytokine NM_080867 signaling 4 SOCS5 CK suppressor NM_014011 pathwayof cytokine signaling 5 SOCS6 CK suppressor NM_004232 pathway ofcytokine signaling 6 SOCS7 CK suppressor NM_014598 pathway of cytokinesignaling 7 SOD1 anti-oxidant anti- NM_000454 oxidant SOD2 anti-oxidantanti- NM_001024465| oxidant NM_001024466| NM_000636 SOD3 anti-oxidantanti- NM_003102 oxidant SOS1 TCR NM_005633 pathway SOX17 SOX17 SOX17 SRY(sex NM_022454 determining region Y)- box 17 CD43 Adaptive immunityNM_003123 ST14 suppression of protease, cytotox NM_021978 tumorigenicity14 (colon carcinoma, matriptase, epithin) STAM STAM STAM signalNM_003473 transducing adaptor molecule (SH3 domain and ITAM motif) 1STAT1 STAT1 STAT1 Signal NM_139266| transducer and NM_007315 activatorof transcription 1, 91 kDa STAT2 STAT2 STAT2 signal transducer andactivator of transcription 2, 113 kDa STAT3 STAT3 STAT3 SignalNM_213662| transducer and NM_139276| activator of NM_003150transcription 3 (acute-phase response factor) STAT4 STAT4 STAT4 signalNM_003151 transducer and activator of transcription 4 STAT5A STAT5ASTAT5A signal NM_003152 transducer and activator of transcription 5ASTAT5B STAT5B STAT5B signal NM_012448 transducer and activator oftranscription 5B STAT6 STAT6 STAT6 signal NM_003153 transducer andactivator of transcription 6, interleukin-4 induced STK36 STK36 STK36serine/threonine NM_015690 kinase 36 (fused homolog, Drosophila) TAP1transporter 1, MHC pathway NM_000593 ATP-binding cassette, sub- family B(MDR/TAP) TAP2 transporter 2, MHC transporter 2, NM_000544 ATP-bindingpathway ATP-binding cassette, sub- cassette, sub- family B family B(MDR/TAP) (MDR/TAP) TBX21 T-bet (TBX21) Adaptive immunity NM_013351 TCF7TCF7 wnt transcription NM_201633| pathway factor 7 (T-cell NM_201632|specific, HMG- NM_201634| box) NM_213648| NM_003202 TERT NM_198255|NM_003219 TFRC CD71 endogenous endogenous NM_003234 control control TGFANM_003236 TGFB1 TGF-b/BIGH3 TGF pathway NM_000660 TGFBR1 TGFBR1 TGFtransforming NM_004612 pathway growth factor, beta receptor I (activin Areceptor type II-like kinase, 53 kDa) TGFBR2 TGFBR2 TGF transformingNM_001024847| pathway growth factor, NM_003242 beta receptor II (70/80kDa) TIMP3 TIMP TGF adhesion, NM_000362 metallopeptidase pathwaymetastasis inhibitor 3 (Sorsby fundus dystrophy, pseudoinflammatory)TLR1 TLR1 Toll-like toll-like NM_003263 pathway receptor 1 TLR10 TLR10Toll-like toll-like NM_030956 pathway receptor 10 TLR2 TLR2 Toll-liketoll-like NM_003264 pathway receptor 2 TLR3 TLR3 Toll-like toll-likeNM_003265 pathway receptor 3 TLR4 TLR4 Toll-like toll-like NM_138554|pathway receptor 4 NM_003266 TLR5 TLR5 Toll-like toll-like NM_003268pathway receptor 5 TLR6 TLR6 Toll-like toll-like NM_006068 pathwayreceptor 6 TLR7 TLR7 Toll-like toll-like NM_016562 pathway receptor 7TLR8 TLR8 Toll-like toll-like NM_138636 pathway receptor 8 TLR9 TLR9Toll-like toll-like NM_017442| pathway receptor 9 NM_138688 TNF TNF-aTNF-a DIF, TNFA, NM_000594 TNFSF2, CACHECTIN TNFRSF10A TRAILR1 apoptosispathway NM_003844 TNFRSF11A RANK Adaptive immunity NM_003839 TNFRSF18GITR (TNFRSF18) Adaptive immunity NM_004195 TNFRSF1A TNF RI Adaptiveimmunity NM_001065 TNFRSF1B TNF RII Adaptive immunity NM_001066 OX-40NM_003327 TNFRSF5 CD40 Adaptive immunity NM_001250, NM_152854 TNFRSF6Fas Apoptosis FAS, APT1, NM_000043 pathway CD95, APO- 1, FASTM TNFRSF7CD27 Adaptive immunity NM_001242 TNFRSF8 CD30 Adaptive immunityNM_152942| NM_001243 TNFRSF9 4-1BB Adaptive immunity NM_001561 TNFSF10TRAIL tumor NM_003810 necrosis factor (ligand) superfamily, member 10TNFSF6 FasL apopyosis FASL, FasL, NM_000639 pathway CD178, CD95L,APT1LG1 TOB1 transducer of TCR pathway transducer of NM_005749 ERBB2ERBB2, 1 TP53 tumor p53 pathway NM_000546 protein p53 (Li-Fraumenisyndrome) TSLP Adaptive immunity NM_033035| NM_138551 VCAM1 vascularcell MGC99561, INCAM- metastasis NM_001078.2 adhesion 100,DKFZp779G2333, signature molecule 1, Gene HGNC: hCG32384 Celera 12663Annotation VEGF VEGF A VEGF A NM_003376 WIF1 wnt pathway inhibitoryfactor 1 NM_007191 WNT1 wnt pathway wingless-type NM_005430 MMTVintegration site family, member 1 WNT4 wingless-type wnt pathwaywingless-type NM_030761 MMTV integration MMTV integration site family,site family, member 4 member 4 XCL1 Lymphotactin chemokine chemokine (CNM_002995 pathway motif) ligand 1 XCR1 chemokine chemokine (CNM_001024644| pathway motif) receptor 1 NM_005283 ZAP70 ZAP70 TCRpathway zeta-chain (TCR) NM_001079 associated protein kinase 70 kDa ZIC2hedgehog Zic family NM_007129 pathway Zic member 2 (odd- family pairedhomolog, member 2 Drosophila) (odd-paired homolog, Drosophila)

TABLE 10 Access. No Name Description Type Location 2 transcriptsTNFRSF6B tumor necrosis factor hCG22751: 20q13.3 receptor superfamily, 7transcripts member 6b, decoy NM_001712 CEACAM1 CarcinoembryonichCG21881: 19q13.2, antigen-related cell 2 transcripts adhesion molecule1 (biliary glycoprotein) NM_014143 PDCD1LG1 programmed cell death 1hCG27938: 9p24, ligand 1 1 transcripts 2 transcripts CD8A CD8 antigen,alpha hCG34192: 2p12 polypeptide (p32) 3 transcripts NM_000963 PTGS2Prostaglandin- hCG39885: 1q25.2- endoperoxide synthase 2 1 transcriptsq25.3, (prostaglandin G/H synthase and cyclooxygenase) NM_001168 BIRC5baculoviral IAP repeat- hCG27811: 17q25, containing 5 (survivin) 4transcripts NM_000655 SELL selectin L (lymphocyte hCG37088: 1q23-q25,adhesion molecule 1) 5 transcripts NM_002164 INDO indoleamine-pyrrole2,3 hCG27061: 8p12-p11, dioxygenase 2 transcripts NM_016123 IRAK4interleukin-1 receptor- hCG39494: 4, associated kinase 4 2 transcriptsNM_000594 TNF tumor necrosis factor hCG43716: 6p21.3, (TNF superfamily,1 transcripts member 2) NM_003844 TNFRSF10A tumor necrosis factorhCG31588: 8p21, receptor superfamily, 1 transcripts member 10a NM_002423MMP7 Matrix metalloproteinase hCG1640914: 11q21- 7 (matrilysin, uterine)1 transcripts q22, NM_006864 LILRB3 Leukocyte hCG2009348: 19q13.4,immunoglobulin-like 1 transcripts receptor, subfamily B (with TM andITIM domains), member 3 2 transcripts CD3Z CD3Z antigen, zetahCG1769040: 1q22-q23, polypeptide (TiT3 2 transcripts complex) 2transcripts TNFRSF8 tumor necrosis factor hCG25063: 1p36, receptorsuperfamily, 1 transcripts member 8 NM_002046 GAPD glyceraldehyde-3-hCG2005673: 12p13, phosphate 1 transcripts dehydrogenase NM_001565CXCL10 chemokine (C-X-C motif) hCG23842: 4q21, ligand 10 2 transcripts 2transcripts EBAG9 estrogen receptor hCG15046: 8q23, binding siteassociated, 2 transcripts antigen, 9 NM_000584 IL8 interleukin 8hCG16372: 4q13-q21 2 transcripts 2 transcripts STAT1 signal transducerand hCG25794: 2q32.2, activator of transcription 6 transcripts 1, 91 kDaNM_001504 CXCR3 chemokine (C-X-C motif) hCG19964: Xq13, receptor 3 1transcripts NM_000660 TGFB1 transforming growth hCG22321: 19q13.2,factor, beta 1 (Camurati- 2 transcripts Engelmann disease) NM_012092ICOS inducible T-cell co- hCG1642889: 2q33, stimulator 1 transcriptsNM_002416 CXCL9 chemokine (C-X-C motif) hCG1781951: 4q21, ligand 9 1transcripts 2 transcripts CD97 CD97 antigen hCG27517: 19p13, 5transcripts NM_003853 IL18RAP interleukin 18 receptor hCG28161: 2p24.3-accessory protein 1 transcripts p24.1 NM_006564 CXCR6 chemokine (C-X-Cmotif) hCG15326: 3p21, receptor 6 1 transcripts NM_004314 ART1ADP-ribosyltransferase 1 hCG16165: 11p15, 1 transcripts NM_002198 IRF1interferon regulatory hCG24115: 5q31.1, factor 1 1 transcripts NM_025240B7H3 B7 homolog 3 hCG40826: 15q23- 1 transcripts q24, 3 transcripts ACEangiotensin I converting hCG41821: 17q23, enzyme (peptidyl- 5transcripts dipeptidase A) 1 NM_003855 IL18R.1 interleukin 18 receptor 1hCG28160: 2q12 3 transcripts NM_013351 TBX21 T-box 21 hCG27200: 17q21.2,1 transcripts NM_001562 IL18 interleukin 18 hCG39294: 11q22.2-(interferon-gamma- 2 transcripts q22.3 inducing factor) NM_005018 PDCD1programmed cell death 1 hCG1776289: 2q37.3, 1 transcripts NM_000619 IFNGinterferon, gamma hCG15987: 12q14, 1 transcripts 2 transcripts GNLYGranulysin hCG32948: 2p12-q11, 3 transcripts NM_002051 GATA3 GATAbinding protein 3 hCG23634: 10p15, 3 transcripts NM_003376 VEGF vascularendothelial hCG18998: 6p12 growth factor 7 transcripts NM_004131 GZMBgranzyme B (granzyme hCG40183: 14q11.2, 2, cytotoxic T- 1 transcriptslymphocyte-associated serine esterase 1) NM_014387 LAT linker foractivation of T hCG2039637: 16q13, cells 5 transcripts NM_000616 CD4 CD4antigen (p55) hCG25949: 12pter- 2 transcripts p12, NM_031281 IRTA2immunoglobulin hCG39827: 1q21, superfamily receptor 3 transcriptstranslocation associated 2 NM_000572 IL10 interleukin 10 hCG22208:1q31-q32, 1 transcripts NM_003326 TNFSF4 tumor necrosis factor hCG37644:1q25, (ligand) superfamily, 2 transcripts member 4 (tax-transcriptionally activated glycoprotein 1, 34 kDa) NM_018676 THSD1thrombospondin, type I, hCG29569: 13q14.13, domain 1 4 transcriptsNM_025239 PDCD1LG2 programmed cell death 1 hCG1641650: 9p24.2, ligand 21 transcripts

TABLE 11 Table S1: Characteristics of the three cohorts of patientsCohorts HEGP Avicenne HEGP-2 Characteristic No. of Patients Tumor (T)stage^(†) pT1 23 3 2 pT2 69 13 7 pT3 218 76 57 pT4 97 27 9 Nodal (N)status Negative 241 48 30 Positive 166 71 43 Nx^(‡) 2 Distant metastases(M) None detected 313 93 47 Present 94 26 28 UICC-TNM Classification(25) I 75 12 6 II 137 33 17 III 100 48 24 IV 95 26 28 Sex Male 216 57 42Female 191 62 33 Location Colon 162 35 41 Sigmoid 114 53 25 Rectum 13131 9 Differentiation Well 312 78 47 Poor 95 41 28 ^(†)The stage wasdetermined by pathological (p) examination. T1 tumor invading submucosa,T2 tumor invading muscularis propria, T3 tumor penetrating muscularispropria and invading subserosa, and T4 tumor invading other organs orstructures or perforating visceral peritoneum. ^(‡)It was not possibleto determine the nodal status of two patients.

TABLE 12 Table S3: Disease-free survival analyses for all patientsaccording to the minimum P-value cut-offs Disease-Free survival (DFS)for all patients Rate No. of Median Rate at Rate at at patients months 2yr % 4 yr % 5 yr % P value* P value P value^(a) GZMB_(CT) 1.78 10⁻⁴ 9.7010⁻⁵ 4.85 10⁻⁵ Lo 191 34.6 53.8 46.1 46.1 Hi 163 NR 76.6 73.1 72.1GZMB_(IM) 1.99 10⁻⁵ 1.28 10⁻⁵ 5.38 10⁻⁶ Lo 109 20.1 47.1 41.0 41.0 Hi195 NR 75.4 70.9 70.0 CD45RO_(CT) 4.33 10⁻⁵ 9.81 10⁻⁵ 1.27 10⁻⁴ Lo 4511.5 27.5 24.0 24.0 Hi 261 NR 72.0 65.5 64.9 CD45RO_(IM) 3.71 10⁻³ 4.2910⁻⁴ 4.41 10⁻⁴ Lo 168 41.5 55.4 47.3 47.3 Hi 145 NR 76.8 72.9 71.9CD8_(CT) 5.19 10⁻⁶ 3.44 10⁻⁶ 1.45 10⁻⁶ Lo 132 21.0 46.0 39.1 39.1 Hi 227NR 74.9 69.5 68.8 CD8_(IM) 1.03 10⁻² 1.11 10⁻² 8.09 10⁻³ Lo 186 45.556.4 49.1 49.1 Hi 128 NR 77.8 73.5 72.3 CD3_(CT) 5.48 10⁻⁶ 1.38 10⁻⁵1.09 10⁻⁵ Lo 165 23.1 48.2 43.4 43.4 Hi 192 NR 79.8 72.4 71.6 CD3_(IM)4.78 10⁻⁶ 6.23 10⁻⁶ 6.32 10⁻⁶ Lo 175 31.1 51.8 43.5 43.5 Hi 178 NR 78.774.7 73.9 GZMB_(CT/IM) 3.32 10⁻⁴ 8.34 10⁻⁵ 5.45 10⁻⁵ Lo 70 20.1 46.339.1 39.1 Het 98 77.5 62.2 54.4 54.4 Hi/Hi 102 NR 80.6 77.8 76.3CD45RO_(CT/IM) 6.61 10⁻⁶ 1.05 10⁻⁵ 1.69 10⁻⁵ Lo/Lo 32  5.85 24.2 24.224.2 Het 141  63.28 61.2 50.7 50.7 Hi/Hi 127 NR 80.3 78.1 76.9CD8_(CT/IM) 3.80 10⁻⁵ 7.70 10⁻⁵ 5.52 10⁻⁵ Lo/Lo 93 20.1 44.3 35.0 35.9Het 94 NR 63.8 58.9 58.9 Hi/Hi 96 NR 81.6 76.1 74.6 CD3_(CT/IM) 7.5610⁻⁸ 1.22 10⁻⁷ 1.16 10⁻⁷ Lo/Lo 93 17.3 42.8 37.4 37.4 Het 116 68.7 61.751.8 51.8 Hi/Hi 109 NR 87.6 84.0 82.7 *Log-ranked P value corrected(Altman et al. 1994), P value median 100 * CV Log-rank; ^(a)P valuemedian 100 * CV stratified Log-rank; NR: Not Reached; Het: Hi/Lo andLo/H

TABLE 13 Table S4: Overall survival analyses for all patients accordingto the minimum P-value cut- offs Overall survival (OS) for all patientsRate No. of Median Rate at Rate at at patients months 2 yr % 4 yr % 5 yr% P value* P value P value^(a) GZMB_(CT) 8.18 10⁻⁷ 1.29 10⁻⁵ 1.16 10⁻⁵Lo 191 42.7 62.2 47.3 40.1 Hi 163 NR 83.8 76.0 68.4 GZMB_(IM) 5.55 10⁻³8.39 10⁻² 6.77 10⁻² Lo 109 43   68.6 47.3 39.9 Hi 195 89.0 74.6 66.757.8 CD45RO_(CT) 5.77 10−⁷ 8.99 10⁻⁵ 6.57 10⁻⁵ Lo 45 25.6 55.5 26.9 20.9Hi 261 100.7  74.9 66.3 58.7 CD45RO_(IM) 1.65 10⁻³ 8.20 10⁻³ 1.07 10⁻²Lo 168 46.9 67.1 49.4 42.0 Hi 145 NR 78.6 73.3 65.5 CD8_(CT) 2.66 10⁻⁷6.66 10⁻⁶ 3.79 10⁻⁶ Lo 132 35.1 60.3 41.8 36.5 Hi 227 NR 79.8 71.0 62.3CD8_(IM) 1.22 10⁻³ 1.51 10⁻² 1.50 10⁻² Lo 186 51.3 70.2 51.6 43.0 Hi 128NR 78.4 72.6 66.1 CD3_(CT) 7.86 10⁻⁸ 1.35 10⁻⁵ 9.65 10⁻⁶ Lo 165 33.557.9 42.4 35.6 Hi 192 115.1  83.2 74.3 66.4 CD3_(IM) 9.08 10⁻⁵ 1.83 10⁻⁴1.46 10⁻⁴ Lo 175 46.9 65.9 49.3 40.3 Hi 178 NR 77.8 70.2 64.0GZMB_(CT/IM) 2.00 10⁻⁴ 1.67 10⁻² 1.60 10⁻² Lo 70 50.8 72.4 50.7 45.2 Het98 40.3 60.8 48.0 35.4 Hi/Hi 102 NR 84.1 77.8 70.8 CD45RO_(CT/IM) 8.5610⁻⁶ 3.34 10⁻⁴ 2.91 10⁻⁴ Lo/Lo 32 28.4 60.6 22.5 22.5 Het 141 51.5 66.354.7 44.1 Hi/Hi 127 NR 81.2 76.1 70.5 CD8_(CT/IM) 6.25 10⁻⁶ 4.69 10⁻⁴4.28 10⁻⁴ Lo/Lo 93 35.1 61.2 40.0 33.8 Het 94 59.6 74.6 61.0 49.3 Hi/Hi96 NR 82.3 75.9 69.2 CD3_(CT/IM) 3.97 10⁻⁷ 2.11 10⁻⁶ 1.37 10⁻⁶ Lo/Lo 9335.1 59.3 40.7 29.9 Het 116 57.8 68.0 54.7 49.5 Hi/Hi 109 NR 86.4 80.872.6 *Log-ranked P value, P value median 100 * CV Log-rank; ^(a)P valuemedian 100 * CV stratified Log-rank; NR: Not Reached; Het: Hi/Lo andLo/Hi

TABLE 14 Table S5: Disease-free survival analyses for all patientsaccording median cut-offs Disease-Free Survival (DFS) for all patientsRate No. of Median Rate at Rate at at 5 patients months 2 yr % 4 yr % yr% P value* GZMB_(CT) 9.94 10⁻⁷ Lo 190 34.6 53.5 45.7 45.7 Hi 164 NR 76.873.2 72.3 GZMB_(IM) 1.99 10⁻⁶ Lo 152 31.1 51.0 46.6 46.6 Hi 152 NR 79.573.6 72.5 CD45RO_(CT) 2.21 10⁻² Lo 153 77.4 59.3 54.2 54.2 Hi 153 NR72.9 65.8 64.8 CD45RO_(IM) 1.85 10⁻⁴ Lo 158 36.0 53.7 48.0 48.0 Hi 155NR 77.0 70.7 69.7 CD8_(CT) 3.06 10⁻⁷ Lo 180 31.3 51.1 45.4 45.4 Hi 179NR 78.0 72.1 71.2 CD8_(IM) 2.72 10⁻⁴ Lo 157 36.0 56.3 47.7 47.7 Hi 157NR 74.3 70.7 69.7 CD3_(CT) 1.65 10⁻⁶ Lo 179 31.3 52.0 46.7 46.7 Hi 178NR 78.7 71.6 70.8 CD3_(IM) 2.98 10⁻⁸ Lo 177 31.1 51.8 43.6 43.6 Hi 176NR 79.1 75.1 74.2 GZMB_(CT/IM) 5.07 10⁻⁶ Lo 91 23.0 46.6 40.9 40.9 Het93 NR 67.2 59.2 59.2 Hi/Hi 86 NR 82.1 78.7 77.0 CD45RO_(CT/IM) 7.60 10⁻⁴Lo/Lo 92 35.8 52.6 49.5 49.5 Het 119 100.2  63.7 54.4 54.4 Hi/Hi 89 NR81.7 77.3 75.8 CD8_(CT/IM) 7.75 10⁻⁷ Lo/Lo 98 20.8 44.9 37.3 37.3 Het 92NR 67.8 61.1 61.1 Hi/Hi 93 NR 79.7 75.4 73.8 CD3_(CT/IM) 3.52 10⁻⁹ Lo/Lo98 18.2 44.9 30.7 39.7 Het 119 68.7 62.5 53.2 53.2 Hi/Hi 101 NR 87.884.0 82.5 *Log-ranked P value for median cut-off; NR: Not Reached; Het:Hi/Lo and Lo/H

TABLE 15 Table S6: Overall survival analyses for all patients accordingmedian cut-offs Overall Survival (OS) for all patients Rate No. ofMedian Rate at Rate at at 5 patients months 2 yr % 4 yr % yr % P value*GZMB_(CT) 8.18 10⁻⁷ Lo 191 42.7 62.2 47.3 40.1 Hi 163 NR 83.8 76.0 68.4GZMB_(IM) 1.55 10⁻² Lo 152 50.8 67.4 50.7 44.3 Hi 152 100.8  77.5 68.958.2 CD45RO_(CT) 6.92 10⁻⁴ Lo 153 46.9 65.7 49.1 43.6 Hi 153 111.3  78.672.0 62.9 CD45RO_(IM) 1.49 10⁻³ Lo 158 43.1 66.3 48.4 41.5 Hi 155 115.0 78.6 72.8 64.6 CD8_(CT) 2.05 10⁻⁵ Lo 180 41.7 65.8 45.7 41.1 Hi 179 NR79.6 74.9 64.8 CD8_(IM) 2.16 10⁻³ Lo 157 50.8 71.9 51.0 42.0 Hi 157 NR75.3 69.5 63.2 CD3_(CT) 7.04 10⁻⁶ Lo 179 39.2 61.5 45.6 38.5 Hi 178115.1  82.5 73.7 66.1 CD3_(IM) 9.38 10⁻⁵ Lo 177 46.9 66.4 49.3 40.5 Hi176 NR 77.0 70.5 64.2 GZMB_(CT/IM) 2.88 10⁻³ Lo 92 46.9 67.4 49.6 45.4Het 92 51.3 66.9 54.4 39.3 Hi/Hi 86 115.1  83.9 76.5 69.9 CD45RO_(CT/IM)7.43 10⁻⁵ Lo/Lo 92 40.8 65.2 45.3 41.0 Het 119 52.9 66.7 55.1 45.3 Hi/Hi89 NR 85.9 83.0 75.4 CD8_(CT/IM) 1.04 10⁻⁴ Lo/Lo 98 36.4 66.0 42.3 36.8Het 92 59.6 72.6 58.7 46.9 Hi/Hi 93 NR 80.2 77.5 70.3 CD3_(CT/IM) 2.0810⁻⁶ Lo/Lo 98 35.1 60.3 41.3 31.0 Het 119 60.0 69.7 56.7 50.5 Hi/Hi 101NR 85.4 80.6 73.0 *Log-ranked P value for median cut-off; NR: NotReached; Het: Hi/Lo and Lo/Hi

TABLE 16 Table S7: Disease-free survival analyses for UICC-TNM, I, II,III patients according to the minimum P-value cut-offs Disease-FreeSurvival (DFS) for UICC-TNM, I, II, III patients No. of Median Rate atRate at patients months 2 yr % 4 yr % Rate at 5 yr % P value* P value Pvalue^(a) GZMB_(CT) 9.17 10⁻¹ 3.87 10⁻¹ 3.99 10⁻¹ Lo 166 NR 77.0 68.268.2 Hi 107 NR 87.7 83.7 83.7 GZMB_(IM) 3.47 10⁻¹ 1.51 10⁻¹ 1.46 10⁻¹ Lo126 NR 74.1 68.7 68.7 Hi 112 NR 89.1 82.5 82.5 CD45RO_(CT) 3.58 10⁻³2.01 10⁻³ 1.69 10⁻³ Lo 25 23.1 42.4 37.1 37.1 Hi 218 NR 84.4 77.3 77.3CD45RO_(IM) 3.49 10⁻¹ 1.66 10⁻¹ 1.73 10⁻¹ Lo 121 NR 74.2 64.4 64.4 Hi125 NR 85.9 81.5 81.5 CD8_(CT) 5.62 10⁻⁴ 1.76 10⁻⁴ 1.58 10⁻⁴ Lo 85 77.564.1 55.7 55.7 Hi 193 NR 88.3 82.0 82.0 CD8_(IM) 2.20 10⁻¹ 1.43 10⁻¹1.81 10⁻¹ Lo 138 NR 74.0 65.5 65.5 Hi 111 NR 87.9 82.9 82.9 CD3_(CT)1.93 10⁻⁴ 1.22 10⁻³ 1.16 10⁻³ Lo 69 68.7 62.6 53.8 53.8 Hi 209 NR 87.980.9 80.9 CD3_(IM) 4.78 10⁻⁶ 6.23 10⁻⁶ 6.32 10⁻⁶ Lo 103 NR 72.0 60.560.5 Hi 171 NR 88.1 83.3 83.3 GZMB_(CT/IM) 7.81 10⁻¹ 3.33 10⁻¹ 3.43 10⁻¹Lo 87 NR 70.4 64.3 64.3 Het 69 NR 81.7 73.2 73.2 Hi/Hi 58 NR 90.8 85.985.9 CD45RO_(CT/IM) 1.45 10⁻² 4.04 10⁻² 4.14 10⁻² Lo/Lo 16 20.1 40.240.2 40.2 Het 110 NR 76.8 64.8 64.8 Hi/Hi 112 NR 88.3 85.9 85.9CD8_(CT/IM) 1.94 10⁻³ 3.52 10⁻³ 3.84 10⁻³ Lo/Lo 61 35.8 59.0 48.9 48.9Het 76 NR 82.2 76.2 76.2 Hi/Hi 87 NR 87.8 81.6 81.6 CD3_(CT/IM) 8.5710⁻⁷ 2.49 10⁻⁴ 2.93 10⁻⁴ Lo/Lo 30 23.1 49.9 38.0 38.0 Het 95 NR 76.465.6 65.6 Hi/Hi 124 NR 91.3 87.1 87.1 *Log-ranked P value, P valuemedian 100 * CV Log-rank; ^(a)P value median 100 * CV stratifiedLog-rank; NR: Not Reached; Het: Hi/Lo and Lo/Hi

TABLE 17 Table S8: Overall survival analyses for UICC-TNM, I, II, IIIpatients according to the minimum P-value cut-offs Overall Survival (OS)for UICC-TNM, I, II, III patients No. of Median Rate at Rate at Rate atpatients months 2 yr % 4 yr % 5 yr % P value* P value P value^(a)GZMB_(CT) 1.57 10⁻¹ 1.13 10⁻¹ 8.16 10⁻² Lo 166 89.0 83.5 69.2 61.1 Hi107 NR 85.9 79.7 71.5 GZMB_(IM) 5.38 10⁻¹ 7.30 10⁻¹ 7.16 10⁻¹ Lo 126101.2  81.7 69.1 61.8 Hi 112 115.0  85.6 76.2 64.5 CD45RO_(CT) 2.53 10⁻⁴2.92 10⁻³ 2.25 10⁻³ Lo 25 43.0 69.7 41.8 32.5 Hi 218 NR 83.9 75.4 67.8CD45RO_(IM) 1.22 10⁻¹ 3.86 10⁻¹ 4.33 10⁻¹ Lo 121 101.0  80.1 64.2 56.5Hi 125 NR 84.3 79.4 70.8 CD8_(CT) 6.00 10⁻⁵ 1.09 10⁻⁴ 1.03 10⁻⁴ Lo 8565.3 74.8 56.1 50.7 Hi 193 NR 88.2 81 71.8 CD8_(IM) 3.03 10⁻² 4.32 10⁻¹4.20 10⁻¹ Lo 138 76.1 82.4 66.3 57 Hi 111 NR 86.8 81.2 73.7 CD3_(CT)2.51 10⁻⁷ 1.65 10⁻⁵ 1.47 10⁻⁵ Lo 69 35.5 69.5 47.6 40.1 Hi 209 NR 88.581.4 73.2 CD3_(IM) 7.35 10⁻³ 7.61 10⁻² 6.25 10⁻² Lo 103 69.3 80.7 6553.7 Hi 171 NR 85.9 78.7 71.7 GZMB_(CT/IM) 8.29 10⁻² 5.30 10⁻¹ 4.99 10⁻¹Lo 87 NR 83.2 69.2 63.3 Het 69 69.3 80.8 67.7 51.8 Hi/Hi 58 NR 85.6 79.271.8 CD45RO_(CT/IM) 1.02 10⁻² 4.39 10⁻² 3.72 10⁻² Lo/Lo 16 43.0 73.136.5 36.5 Het 110 87.3 80.1 67.3 56.3 Hi/Hi 112 NR 85.3 80.8 74.8CD8_(CT/IM) 7.02 10⁻⁴ 1.60 10⁻² 1.96 10⁻² Lo/Lo 61 56.2 72.7 52.2 45.3Het 76 111.4  88.6 77.3 65.1 Hi/Hi 87 NR 86.7 81.1 73.6 CD3_(CT/IM) 2.9010⁻⁶ 2.72 10⁻⁴ 1.21 10⁻⁴ Lo/Lo 30 35.5 71.4 43.4 32.6 Het 95 72.1 76.664.6 54.8 Hi/Hi 124 NR 90.8 85.9 78.6 *Log-ranked P value, P valuemedian 100 * CV Log-rank; ^(a)P value median 100 * CV stratifiedLog-rank; NR: Not Reached; Het: Hi/Lo and Lo/Hi

TABLE 18 Table S9: DFS analyses for UICC-TNM, I, II, III patientsaccording median cut-offs Disease-Free Survival (DFS) for all patientsRate No. of Median Rate at Rate at at 5 patients months 2 yr % 4 yr % yr% P value* GZMB_(CT) 8.07 10⁻² Lo 140 NR 77.8 68.1 68.1 Hi 133 NR 84.880.6 80.6 GZMB_(IM) 2.26 10⁻² Lo 119 NR 74.7 69.1 69.1 Hi 119 NR 87.981.5 81.5 CD45RO_(CT) 2.27 10⁻¹ Lo 122 NR 76.5 70.7 70.7 Hi 121 NR 84.175.9 75.9 CD45RO_(IM) 1.96 10⁻² Lo 124 NR 74.6 65.1 65.1 Hi 122 NR 85.681.1 81.1 CD8_(CT) 6.53 10⁻⁴ Lo 139 NR 73.3 65.1 65.1 Hi 139 NR 89.483.9 83.9 CD8_(IM) 3.99 10⁻² Lo 125 NR 75.9 66.5 66.5 Hi 124 NR 84.780.1 80.1 CD3_(CT) 4.48 10⁻⁴ Lo 139 NR 72.6 66.1 66.1 Hi 139 NR 91.183.1 83.1 CD3_(IM) 4.42 10⁻⁴ Lo 138 NR 74.4 65.4 65.4 Hi 136 NR 90.184.9 84.9 GZMB_(CT/IM) 5.63 10⁻² Lo 72 NR 69.4 61.9 61.9 Het 74 NR 85.677.8 77.8 Hi/Hi 68 NR 84.1 79.8 79.8 CD45RO_(CT/IM) 6.94 10⁻² Lo/Lo 71NR 72.9 67.0 67.0 Het 100 NR 79.4 68.9 68.9 Hi/Hi 67 NR 87.6 85.6 85.6CD8_(CT/IM) 3.36 10⁻³ Lo/Lo 80 100.3 66.8 57.8 57.8 Het 65 NR 82.9 75.675.6 Hi/Hi 79 NR 86.4 81.3 81.3 CD3_(CT/IM) 6.20 10⁻⁵ Lo/Lo 73 100.365.4 58.8 58.8 Het 102 NR 82.4 73.1 73.1 Hi/Hi 74 NR 94.2 89.1 89.1*Log-ranked P value for median cut-off; NR: Not Reached; Het: Hi/Lo andLo/Hi

TABLE 19 Table S10: OS analyses for UICC-TNM, I, II III patientsaccording median cut-offs Overall Survival (OS) for UICC-TNM, I, II IIIpatients Rate No. of Median Rate at Rate at at 5 patients months 2 yr %4 yr % yr % P value* GZMB_(CT) 5.06 10⁻² Lo 140 27.1 81.2 66.7 59.0 Hi133 NR 87.8 80.0 71.5 GZMB_(IM) 7.28 10⁻¹ Lo 119 30.4 83.3 70.2 62.5 Hi119 115.1  83.7 74.8 63.7 CD45RO_(CT) 2.13 10⁻² Lo 122 21.4 79.0 63.557.1 Hi 121 NR 86.0 80.8 71.4 CD45RO_(IM) 1.31 10⁻¹ Lo 124 21.4 79.864.3 56.8 Hi 122 NR 84.7 79.7 70.8 CD8_(CT) 8.49 10⁻³ Lo 139 24.8 82.563.9 55.7 Hi 139 NR 85.9 82.9 74.6 CD8_(IM) 7.20 10⁻² Lo 125 30.0 84.267.6 57.3 Hi 124 NR 84.6 78.5 71.8 CD3_(CT) 9.15 10⁻³ Lo 139 18.9 78.062.5 54.8 Hi 139 NR 89.4 83.0 74.6 CD3_(IM) 2.80 10⁻² Lo 138 26.0 82.767.1 58.7 Hi 136 NR 85.3 80.5 72.3 GZMB_(CT/IM) 6.09 10⁻¹ Lo 72 111.4 82.6 66.9 63.3 Het 74 76.1 82.3 72.3 54.7 Hi/Hi 68 115.1  84.5 75.5 69.2CD45RO_(CT/IM) 2.48 10⁻² Lo/Lo 71 87.3 78.4 59.2 55.8 Het 100 74.2 81.171.1 58.1 Hi/Hi 67 NR 87.7 85.7 79.9 CD8_(CT/IM) 1.56 10⁻² Lo/Lo 80 59.679.3 60.4 49.6 Het 65 NR 88.5 73.4 64.1 Hi/Hi 79 NR 83.7 82.2 75.5CD3_(CT/IM) 5.35 10⁻³ Lo/Lo 73 59.6 76.0 57.1 48.4 Het 102 78.8 82.973.6 65.0 Hi/Hi 74 NR 90.3 87.1 78.8 *Log-ranked P value for mediancut-off; NR: Not Reached; Het: Hi/Lo and Lo/Hi

Tables 20 (S11) and 21 (S12)

TABLE S11a Multivariate proportional hazard Cox analysis for DFSVariable Hazard ratio 95% CI P value T-stage 1.574 (1.09-2.26) 0.02N-stage^(‡) 1.398 (0.83-2.36) 0.21 Differentiation 0.77 (0.42-1.43) 0.41CD3_(CT)/CD3_(IM) 2.391*  (1.68-3.41)* 1.4 10⁻⁶ patterns 2.379† (1.67-3.39)† *Minimum P-value cut-off with 3 groups (HiHi, LoLo, Het)†Leave-one-out method, correction using C = 1− (SE[coef]/coef)2 =0.9942694 (heuristic shrinkage factor, Holländer et al). Similar resultsobtained using bootstrap ^(‡)Note: N violates the proportional hazardsassumption. A model stratifying by this factor was performed andCD3CT/CD3IM remained an independent prognostic factor for disease freesurvival. P-values from the stratified model: T = 0.01; Diff = 0.32;CD3CT/CD3IM = 9.4 × 10 − 7.0. HR from the stratified model: T =1.572(1.10-2.25); Diff = 0.728(0.39-1.37); CD3CT/CD3IM =2.451(1.71-3.51). Shrinkage factor: c = 0.9583

TABLE S11b Multivariate proportional hazard Cox analysis for DFSVariable Hazard ratio 95% CI P value T-stage 1.370 (0.86-2.19) 0.19N-stage^(‡) 1.210 (0.60-2.47) 0.59 Differentiation 1.020 (0.50-2.08)0.97 CD3_(CT)/CD3_(IM) 6.200*  (2.96-12.99)* 1.3 10⁻⁶ patterns 5.940†  (2.83-12.43)† *Minimum P-value cut-off with 2 groups (HiHi, LoLo)†Leave-one-out method, correction using C = 1− (SE[coef]/coef)² =0.9573126 (heuristic shrinkage factor, Holländer et al). Similar resultsobtained using bootstrap ^(‡)Note: N violates the proportional hazardsassumption. A model stratifying this factor was performed andCD3CT/CD3IM remained an independent prognostic factor for disease freesurvival. P-values from the stratified model: T = 0.02; Diff = 0.97;CD3_(CT)/CD3_(IM) = 1.3 × 10⁻⁶.HR from the stratified model: T =1.408(0.88-2.26); Diff = 0.986(0.48-2.02); CD3_(CT)/CD3_(IM) =6.322(2.99-13.35). Shrinkage factor: c = 0.9573

TABLE S11c Multivariate proportional hazard Cox analysis for DFS inUICC-TNM, I, II, III patients according to median cut-off VariableHazard ratio 95% CI P value T-stage 1.560 (1.074-2.26) 0.02 N-stage^(‡)1.490 (0.882-2.53) 0.14 Differentiation 1.290 (0.682-2.45) 0.43CD3_(CT)/CD3_(IM) 1.870 (1.311-2.66) 5.5 10⁻⁴ patterns§ §cut-off at themedian, with 3 groups (HiHi, LoLo, Het)

TABLE S11d Multivariate proportional hazard Cox analysis for DFS inUICC-TNM, I, II, III patients according to median cut-off VariableHazard ratio 95% CI P value T-stage 1.484 (0.361-6.10) 0.58 N-stage^(‡)2.065 (0.736-5.80) 0.17 Differentiation 1.532 (0.571-4.11) 0.40CD3_(CT)/CD3_(IM) 4.626  (1.736-12.33) 2.2 10⁻³ patterns§ §cut-off atthe median, with 2 groups (HiHi, LoLo)

TABLE S12a Multivariate proportional hazard Cox analysis for OS VariableHazard ratio 95% CI P value T-stage 1.170 (0.90-1.52) 0.25 N-stage^(‡)1.370 (0.90-2.11) 0.15 Differentiation 1.050 (0.68-1.62) 0.84CD3_(CT)/CD3_(IM) 1.890 (1.42-2.51) 1.2 10⁻⁵ patterns* *cut-off at themedian, with 3 groups (HiHi, LoLo, Het)

TABLE S12b Multivariate proportional hazard Cox analysis for OS VariableHazard ratio 95% CI P value T-stage 0.980 (0.69-1.39) 0.91 N-stage^(‡)1.370 (0.75-2.52) 0.31 Differentiation 1.590 (0.97-2.61) 0.07CD3_(CT)/CD3_(IM) 3.710 (1.97-6.97) 4.7 10⁻⁵ patterns* *cut-off at themedian, with 2 groups (HiHi, LoLo)

TABLE S12c Multivariate proportional hazard Cox analysis for OS inUICC-TNM, I, II, III patients according to median cut-off VariableHazard ratio 95% CI P value T-stage 1.200 (0.912-1.57) 0.20 N-stage^(‡)1.450 (0.940-2.22) 0.09 Differentiation 1.080 (0.689-1.68) 0.75CD3_(CT)/CD3_(IM) 1.460 (1.107-1.92) 7.2 10⁻³ patterns§ §cut-off at themedian, with 3 groups (HiHi, LoLo, Het)

TABLE S12d Multivariate proportional hazard Cox analysis for OS inUICC-TNM, I, II, III patients according to median cut-off VariableHazard ratio 95% CI P value T-stage 1.160 (0.818-1.66) 0.40 N-stage^(‡)1.240 (0.696-2.19) 0.47 Differentiation 1.480 (0.892-2.45) 0.13CD3_(CT)/CD3_(IM) 2.200 (1.219-3.97) 8.8 10⁻³ patterns§ §cut-off at themedian, with 2 groups (HiHi, LoLo)

The invention claimed is:
 1. A method for treating a patient sufferingfrom a cancer, comprising the steps of: a) evaluating the intra-tumoradaptative immune status of said patient before administration of ananti-cancer agent by quantifying, in a pre-administration tumor sample,at least two biological markers indicative of the status of theadaptative immune of said patient against cancer, wherein said at leasttwo biological markers are CD8 and CD3; b) administering an anti-canceragent to the patient; c) evaluating the intra-tumor adaptative immunestatus of said patient after administration of said anti-cancer agent byquantifying in a post-administration tumor sample said at least twobiological markers indicative of the status of the adaptative immune ofsaid patient against cancer; d) comparing the levels of said at leasttwo biological markers quantified in step a) with the levels of said atleast two biological markers quantified in step c); e) administering tothe patient: an immuno-stimulatory agent when the level of CD8 has notincreased between step a) and step c); and/or an immuno-stimulatoryagent when the level of CD3 has not increased between step a) and stepc).
 2. The method according to claim 1, wherein step e) furthercomprises administering to said patient an adjuvant therapy.
 3. Themethod according to claim 1, wherein said immuno-stimulatory agent is animmuno-stimulating cytokine or chemokine.
 4. The method according toclaim 3, wherein said cytokine or chemokine is selected from the groupconsisting of IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL 7, G-CSF, IL-15,GM-CSF, IFN-γ, CXCL9, CXCL10, Fractalkine, MIG, IFNα, IL-18, IL-12,IL-23 and IL-31.
 5. The method of claim 4, wherein said cytokine orchemokine is selected from the group consisting of IL-2, IFNα, IL-7,IL-15, and GM-CSF.
 6. The method of claim 1, wherein saidpre-administration and post-administration tumor samples include eitheror both tumor center (CT) tissue from the center of the tumor andinvasive margin (IM) tissue of the invasive margin of the tumor.
 7. Themethod of claim 6, wherein said pre-administration andpost-administration tumor samples are biopsies.
 8. The method of claim1, wherein the cancer is a solid cancer.
 9. The method of claim 1,wherein the cancer is selected from the group consisting of colorectalcancer, colon cancer, rectum cancer, pancreatic cancer, gastrointestinalcarcinoid tumors, stomach cancer, skin cancer, melanoma, lung cancer,bladder cancer, breast cancer, bile duct cancer, laryngeal cancer,hypolaryngeal cancer, nasal cavity cancer, paranasal sinus cancer,nasopharyngeal cancer, oral cavity cancer, oropharyngeal cancer, andsalivary gland cancer.
 10. The method of claim 1, wherein the cancer iscolorectal cancer.
 11. The method according to claim 1, wherein saidanti-cancer agent is a chemotherapeutic agent.
 12. The method accordingto claim 1, wherein said anti-cancer agent is an immunotherapeuticagent.